Ranking nodes by their spreading capability is a recurring requirement in network analysis, but measuring this capability directly through diffusion simulations requires repeated runs over many candidate seed nodes and quickly becomes computationally impractical for large networks. This study addresses a specific question: when diffusion simulations are not allowed, which type of structural information provides the strongest predictive signal for estimating the spreading capability of a node? To answer this question, a multiscale structural feature extraction framework is first developed to characterize each node using 39 descriptors organized into five complementary blocks: local, neighbor, branching, bridge, and global features. Rather than being used as a black-box predictor, this framework serves as a diagnostic tool for identifying the most informative families of structural information. Evaluation on 24 benchmark networks, using SIR-based reference influence, shows that the strongest and most stable influence signal is concentrated in early finite-depth non-backtracking branching patterns. Based on this finding, Diffusion Branch Centrality (DBC) is introduced as a compact and interpretable structural score that combines branching magnitude, diffusion-front growth persistence, and distinct early reach. DBC is specifically designed for simulation-free estimation of SIR-type influence in static, unweighted, and undirected networks, rather than as a universal model for all diffusion processes. The empirical results show that DBC provides accurate and stable influence rankings, improves top-spreader identification, and remains computationally efficient. In addition, ablation analysis, sensitivity analysis, and validation under the Independent Cascade (IC) model provide complementary evidence for the stability of the branching-based principle.

We study the energy-optimal shortest path problem for electric vehicles (EVs) in large-scale road networks, where recuperated energy along downhill segments introduces negative energy costs. While traditional point-to-point pathfinding algorithms for EVs assume a known initial energy level, many real-world scenarios involving uncertainty in available energy require planning optimal paths for all possible initial energy levels, a task known as energy-optimal profile search. Existing solutions typically rely on specialized profile-merging procedures within a label-correcting framework that results in searching over complex profiles. In this paper, we propose a simple yet effective label-setting approach based on multi-objective A* search, which employs a novel profile dominance rule to avoid generating and handling complex profiles. We develop four variants of our method and evaluate them on real-world road networks enriched with realistic energy consumption data. Experimental results demonstrate that our energy profile A* search achieves performance comparable to energy-optimal A* with a known initial energy level.

The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model—MIND: Message Iteration Network Dismantler—generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems.

Detecting anomalous nodes in continuously evolving graphs without labeled supervision requires representations that capture both local temporal context and globally consistent normal behavior—a combination that current methods do not jointly address. Existing dynamic anomaly detectors rely on localized temporal neighborhoods and cannot leverage globally similar normal patterns elsewhere in the graph, while existing retrieval-augmented graph methods either require labels or do not enforce strict temporal causality during retrieval. We propose DGRA-CL (Dynamic Graph Retrieval-Augmented Contrastive Learning), an unsupervised framework that learns discriminative temporal node representations for anomaly detection without labeled data. DGRA-CL transforms dynamic graphs into temporal sequences, employs time- and context-aware contrastive learning to learn normal node behavior patterns, retrieves similar normal exemplars from a training pool under a strict causality constraint, and fuses them via similarity-weighted aggregation to construct baseline representations. Anomalies are detected via deviation-based scoring measuring distance from these normal baselines. On four real-world dynamic graphs, DGRA-CL achieves statistically significant AUC gains of 1–2 points over the strongest baselines on three of four benchmarks (UCI Messages, Bitcoin-Alpha, Digg) and competitive performance on Reddit, while operating without anomaly labels and generalizing to unseen nodes.

Graph anomaly detection identifies abnormal patterns in networks but faces label scarcity and extreme class imbalance. While graph contrastive learning offers unsupervised solutions, existing methods suffer from two limitations: random augmentations break semantic consistency in positive pairs, while naive negative sampling produces trivial contrasts. We propose AC2L-GAD, an Active Counterfactual Contrastive Learning framework addressing both limitations through principled counterfactual reasoning. By combining information-theoretic active selection with counterfactual generation, our approach identifies structurally complex nodes and generates anomaly-preserving positive augmentations alongside hard negative contrasts, while restricting expensive counterfactual generation to a strategically selected subset. This design reduces computational overhead by approximately 65% compared to full-graph counterfactual generation while maintaining detection quality. Experiments on nine benchmark datasets, including real-world financial transaction graphs from GADBench, show that AC2L-GAD achieves competitive or superior performance compared to state-of-the-art baselines, with notable gains in datasets where anomalies exhibit complex attribute-structure interactions.

Identifying influential nodes in complex networks is a fundamental problem with important applications in viral marketing, epidemic control, and social influence analysis. A key challenge in this domain is the scarcity of ground-truth labels: computing accurate influence scores typically requires repeated simulations using diffusion models, which is computationally expensive on large-scale graphs. Additionally, existing deep learning models, particularly those based on graph neural networks (GNNs), often incur high inference costs, limiting their practical deployment. To address these challenges, we propose DistillRWGCN, a knowledge distillation framework based on multi-structural views that captures both structural and local topological features for influence prediction. The framework integrates global and local views of the graph using breadth-first search and depth-first search strategies, fuses the resulting embeddings through an attention mechanism, and distills this knowledge into a lightweight TinyGCN student model via contrastive alignment. DistillRWGCN is trained using only a small subset of influence scores and employs a novel hybrid loss that combines pairwise ranking and Spearman correlation to enhance both prediction accuracy and ranking consistency. Extensive experiments on nine real-world datasets demonstrate that DistillRWGCN achieves up to 23.7 % lower error and 12.5 % higher ranking correlation than state-of-the-art baselines, while significantly reducing inference time. These results highlight the mode’s effectiveness in label-scarce and resource-constrained scenarios.

Recommender systems play a critical role in digital platforms by providing personalized suggestions based on user preferences and interactions. While Graph Neural Networks (GNNs) have improved recommendation accuracy by modeling complex user-item relationships, existing systems often struggle to integrate heterogeneous data sources, including textual reviews, item attributes, and user profiles, into a unified and explainable framework. To address these limitations, we present ExpLMGCN, an explainable multi-modal recommender system that fuses user-item interactions, review embeddings, item features, and user profiles using GNNs and pre-trained language models. Fine-grained user preferences are captured by extracting opinion-aspect pairs (OAs) from reviews and representing them as User–OA and Item–OA bipartite graphs. A contrastive learning framework aligns multi-modal embeddings into a shared latent space, while explanations are generated by ranking OAs based on preference similarity, sentiment, novelty, and factuality. Multiple candidate explanations are produced via large language models, and the optimal one is selected using semantic alignment, multi-aspect coverage, and a self-consistency feedback loop. Extensive experiments on benchmark datasets demonstrate that ExpLMGCN consistently outperforms state-of-the-art baselines, achieving up to 4.39% improvement in recommendation accuracy across Recall and NDCG metrics. Moreover, ExpLMGCN substantially enhances explanation quality, yielding notable gains across BERTScore, ROUGE, and SBERT, and achieving up to 29.4% improvement in BLEU score. These results highlight the model’s ability to provide not only more accurate recommendations but also more coherent and semantically aligned explanations.

Despite Graph Neural Networks (GNNs) having achieved notable success in link prediction tasks, they continue to struggle to model pairwise relationships effectively. Subgraph-based techniques can be effective in small neighborhoods, but they are not scalable, hindering the incorporation of broader structural dependencies. To address this scalability challenge, this paper proposes Pool Walk Subgraph-Based Learning (PWLP) that combines global and local structural information. Our method leverages a pool-walk strategy, incorporating flexible node hops and prioritizing influential nodes within subgraphs, thus capturing both proximal and distant relationships. This architecture effectively integrates structural and node-based features, while mitigating the computational overhead of converting subgraphs to line-graphs by restricting subgraph size through informed candidate node selection, providing scalability across diverse graph settings. Comprehensive evaluations on standard benchmark datasets show that PWLP outperforms state-of-the-art approaches.

Graph- and hypergraph-based recommender systems struggle to learn reliable representations under severe user–item sparsity, particularly in cold-start scenarios. This limitation is compounded by the inefficient integration of auxiliary signals such as social trust networks and user reviews. Existing knowledge distillation methods partially mitigate these challenges but are typically limited to a single teacher and information source, restricting exploitation of heterogeneous information.We model knowledge distillation as a representation alignment process across multiple sources, in which user-centric and item-centric dependencies are extracted and transferred as complementary supervisory signals. The proposed MKDH framework employs two specialised teachers that fuse graph-based structural information with semantic representations from pre-trained BERT. Knowledge from both teachers is transferred to a lightweight HGNN-based student through a contrastive objective, while joint training preserves alignment between the teacher representations and the student’s evolving embeddings. Experiments on Yelp, Ciao, and Epinions show that MKDH consistently outperforms ten strong baselines, achieving statistically significant gains ( ) of up to 2.79% in HR@10 and 4.48% in NDCG@10, with robust performance under highly sparse data. Because only the lightweight student runs at inference, MKDH preserves serving-time efficiency, establishing multi-teacher distillation as an effective paradigm for heterogeneous information in recommendation.

Predicting the influence power of nodes in complex networks, particularly in large-scale scenarios, is a fundamental and challenging problem in network analysis. However, labeling nodes based on their influence power requires running computationally intensive models, such as the Susceptible-Infected-Recovered (SIR) model, which becomes prohibitively time-consuming in large networks, severely limiting scalability. To address this limitation, this study proposes an innovative approach based on multi-level knowledge distillation aimed at enhancing prediction accuracy while substantially reducing inference time, even when few labeled nodes are available. Our approach employs a multi-level teacher-student architecture, enabling knowledge transfer from rich labeled networks to networks with a few labeled nodes. Furthermore, the student model is designed to be shallow, with few parameters, ensuring a lightweight and optimized architecture that significantly reduces the inference time. The transferred knowledge includes both soft labels and an adversarial alignment mechanism between teacher and student models. Experimental results obtained over a range of real-world datasets demonstrate significant improvements in predictive accuracy and computational efficiency.

Power distribution networks, incorporating electric vehicles (EVs) and battery energy storage systems (BESSs), can provide valuable flexibility to the upstream grid. This article proposes a new mechanism for modeling and optimizing two-way flexibility exchange between the distribution system operator (DSO) and flexible loads, aiming to minimize the DSO’s total cost while satisfying the flexibility requests of the upstream market operator. The DSO first solicits the participation of flexible loads, including EVs and BESSs, which can either accept or reject the request. Considering the agreed state of charge of participating resources, a flexibility market is then formulated, incorporating the user contribution index and Karush–Kuhn–Tucker conditions. The proposed mechanism is tested under various load conditions, price tariffs, and EV penetration levels. The results demonstrate significant cost reductions for DSOs, as they purchase less energy from the upstream market operator compared to scenarios without flexibility management.

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale transport networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We first investigate how energy model accuracy influences energy-optimal pathfinding and, consequently, feasibility of planned trips, using a novel data-driven model that incorporates key vehicle dynamics parameters into energy calculations. Additionally, we introduce two novel online reweighting and energy heuristic functions that accelerate path planning with negative energy costs arise due to regenerative braking, making our approach well-suited for real-time applications. Extensive experiments on real-world transport networks demonstrate that our method significantly improves both the computational efficiency of energy-optimal pathfinding for EVs.

The classic Resource Constrained Shortest Path (RCSP) problem aims to find a cost optimal path between a pair of nodes in a network such that the resources used in the path are within a given limit. Having been studied for over a decade, RCSP has seen recent solutions that utilize heuristic-guided search to solve the constrained problem faster. Building upon the bidirectional A* search paradigm, this paper introduces a novel constrained search framework that uses efficient pruning strategies to allow for accelerated and effective RCSP search in large-scale networks. Results show that, compared to the state of the art, our enhanced framework can significantly reduce the constrained search time, achieving speed-ups of over to two orders of magnitude.

Recommender Systems (RS) influence everyday decisions, yet most remain optimized for short-term engagement or commercial gain. RS4SD aims to shift this focus by exploring how RS can contribute to sustainable development through behavioral change and nudging strategies. Aligned with the UN Sustainable Development Goals (SDG), RS4SD will highlight applications that promote responsible consumption, sustainable mobility, healthy eating, and digital well-being. In particular, we will focus on how AI and RS can be designed to foster sustainable behaviors through multi-objective optimization and ethically aligned interventions. These objectives are directly tied to the UN SDG, and we welcome all contributions showcasing RS in support of these goals. A central theme of the workshop is the integration of behavioral science and AI to design interventions that guide users toward more sustainable and healthier choices while preserving individual autonomy. Topics of interest include multi-objective recommendation, health-aware RS, eco-friendly product and tourism RS, as well as novel evaluation metrics that go beyond accuracy to capture societal impact. RS4SD will bring together researchers, stakeholders and practitioners from RS, AI, sustainability, and behavioral science to share models, datasets, frameworks, and real-world use cases. The workshop encourages interdisciplinary collaboration and aims to build a community dedicated to responsible, behavior-aware RS that benefit both individuals and society.

Constrained pathfinding is a classic yet challenging network optimization problem with broad applicability across many real-world domains. The Resource-Constrained Shortest Path (RCSP) problem focuses on finding cost-optimal paths that satisfy multiple resource constraints. In this paper, we propose a novel heuristic-guided search framework that accelerates constrained search in large-scale networks, including those with negative costs and resources, by leveraging efficient queuing and pruning strategies. Experimental results on real-world benchmark maps show that our framework achieves up to two orders of magnitude speedup over state-of-the-art methods, demonstrating its effectiveness in solving challenging RCSP instances within limited time.

The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated superior performance in solving difficult MOSP instances. This paper presents a novel search framework that allows efficient parallelization of MOA* with different objective orders. The framework incorporates a unique upper-bounding strategy that helps the search reduce the problem's dimensionality to one in certain cases. Experimental results demonstrate that the proposed framework can enhance the performance of recent A*-based solutions, with the speed-up proportional to the problem dimension.

Despite its effectiveness, spectral clustering is often impractical for large-scale data due to its high computational complexity$(O(n^{2}))$and limited clustering quality arising from three fundamental limitations: (1) reliance on a fixed similarity graph that cannot adapt to complex local structures, (2) inability to capture higher-order relationships, and (3) a decoupled two-step pipeline that separates embedding and clustering. To address these issues, we propose OA2H-SP, a novel framework that achieves linear-time spectral clustering$(O(nm)$with$m\ll n)$while enhancing clustering accuracy and scalability. Our method constructs an anchor-adaptive hypergraph to model both adaptive and higher-order affinities efficiently. It further unifies representation learning and discrete clustering in a one-step optimization scheme, avoiding the need for k-means post-processing. Extensive experiments on benchmark datasets demonstrate that$\text{OA}^{2}\mathrm{H}$. SP delivers superior performance in terms of accuracy, robustness, and runtime compared to existing hypergraph-based and anchor-driven spectral clustering methods.

In this paper, we propose a novel framework to significantly enhance the inference speed and memory efficiency of Hypergraph Neural Networks (HGNNs) while preserving their high accuracy. Our approach utilizes an advanced teacher-student knowledge distillation strategy. The teacher model, consisting of an HGNN and a Multi-Layer Perceptron (MLP), not only produces soft labels but also transfers structural and high-order information to a lightweight Graph Convolutional Network (GCN) known as TinyGCN. This dual transfer mechanism enables the student model to effectively capture complex dependencies while benefiting from the faster inference and lower computational cost of the lightweight GCN. The student model is trained using both labeled data and soft labels provided by the teacher, with contrastive learning further ensuring that the student retains high-order relationships. This makes the proposed method efficient and suitable for real-time applications, achieving performance comparable to traditional HGNNs but with significantly reduced resource requirements.

This paper proposes SHARP-Distill (\textbf{S}peedy \textbf{H}ypergraph \textbf{A}nd \textbf{R}eview-based \textbf{P}ersonalised \textbf{Distill}ation), a novel knowledge distillation approach based on the teacher-student framework that combines Hypergraph Neural Networks (HGNNs) with language models to enhance recommendation quality while significantly improving inference time. The teacher model leverages HGNNs to generate user and item embeddings from interaction data, capturing high-order and group relationships, and employing a pre-trained language model to extract rich semantic features from textual reviews. We utilize a contrastive learning mechanism to ensure structural consistency between various representations. The student includes a shallow and lightweight GCN called CompactGCN designed to inherit high-order relationships while reducing computational complexity. Extensive experiments on real-world datasets demonstrate that SHARP-Distill achieves up to 68× faster inference time compared to HGNN and 40× faster than LightGCN while maintaining competitive recommendation accuracy.

Coordination between electric vehicles (EVs) and battery systems (BSs) plays a pivotal role in enhancing the flexibility of the electricity grid by offering demand response and energy storage capabilities. This paper proposes a new method for EV-BS coordination to meet the expected flexible load (FL) in each hour of the day. The profit of the Distribution System Operator (DSO) is formulated as a mixed-integer linear programming optimization problem. Additionally, different tariff prices are used to account for the uncertainty of the flexibility price in the electricity market. According to the proposed approach, the flexibility direction is first determined by the DSO, which can be upward flexibility, downward flexibility, or no flexibility, depending on different load conditions. The electricity market is then utilized to provide the expected FLs based on the flexibility direction and the behavior of EVs and BSs, with the participation fee in the proposed program. Simulation results show that the proposed approach increases the DSOs’ profit by considering the coordination between EVs and BSs during non-flexibility hours.

The increasing adoption of electric vehicles (EVs) presents voltage stability challenges in low-voltage (LV) residential distribution networks. EV aggregators can mitigate these issues by coordinating EV charging and discharging while enhancing economic returns through participation in day-ahead market with ancillary services. This paper proposes a novel multi-objective optimization (MOO) approach to maximize the aggregator’s profit including revenues from energy arbitrage, reserve capacity, regulation services and battery degradation costs while simultaneously minimizing voltage deviations within the distribution network. An augmented epsilon-constraint (AUGMENCON) method is implemented to explore the optimal trade-offs between profitability and voltage stability. The implemented method outperforms the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in producing a more non-dominated Pareto front. The methodology is validated on an IEEE 33-bus LV residential network using the MATPOWER toolbox in MATLAB 2024b, demonstrating the feasibility and effectiveness of balancing economic incentives with voltage regulation constraints.

Multi-label learning tasks involve instances that may belong to multiple categories simultaneously, making feature selection particularly challenging in high-dimensional feature spaces. Existing multi-label feature selection methods often suffer from limitations such as high computational complexity, inadequate handling of feature redundancy, and insufficient modelling of label dependencies. To overcome these challenges, we propose a novel framework called Maximum Relevant Minimum Redundant Multi-Label Feature Selection (MR2MLFS), which integrates a two-layer graph representation with a modified Ant Colony Optimization (ACO) strategy. The first graph layer clusters correlated features using Louvain community detection, while the second constructs a meta-graph to model inter-cluster relationships. ACO then explores this structure, favouring the selection of highly relevant and non-redundant features. To reduce computational overhead, we introduce an information-theoretic metric that estimates both feature-label relevance and feature-feature redundancy, eliminating the need for repeated classifier training during the search. We evaluated the proposed method on ten benchmark multi-label datasets using several multi-label classifiers. Experimental results show that the proposed method outperforms six state-of-the-art methods across multiple evaluation metrics, achieving an average relative improvement of 5–12 % while reducing feature dimensionality by up to 80 %. These results confirm the method's robustness, efficiency, and effectiveness in multi-label feature selection.

Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in a sparse matrix. This inherent sparsity poses a challenge because it necessitates accurately and effectively filling in these gaps to provide users with meaningful and personalized recommendations. Our solution addresses sparsity in recommendations by incorporating diverse data sources, including trust statements and an imputation graph. The trust graph captures user relationships and trust levels, working in conjunction with an imputation graph, which is constructed by estimating the missing rates of each user based on the user–item matrix using the average rates of the most similar users. Combined with the user–item rating graph, an attention mechanism fine tunes the influence of these graphs, resulting in more personalized and effective recommendations. Our method consistently outperforms state-of-the-art recommenders in real-world dataset evaluations, underscoring its potential to strengthen recommendation systems and mitigate sparsity challenges. • Triplet path GCN captures nonlinear relationships between users and items. • Sparsity is addressed by adding both imputation and social relation graphs. • Imputation matrix is pre-constructed in preprocessing and used during learning. • Attention mechanism defines graph contributions in the embedded representation space. • Extensive experiments validate the method on two datasets in various settings.

Many existing similarity measures for probabilistic hesitant fuzzy sets (PHFSs) fail to distinguish between the hesitant fuzzy set and probability components, potentially leading to misinterpretations. Since probability and hesitant fuzzy concepts represent different semantic meanings, disregarding these distinctions can result in unrealistic outcomes. To address this issue, we first evaluate the similarity of the probabilistic component of PHFSs and incorporate this value as a coefficient in the similarity measure formula for the hesitant fuzzy component. This approach yields a comprehensive similarity measure that effectively captures both probabilistic and hesitant fuzzy aspects of PHFSs. Building on this framework, we refine traditional set-based similarity measures—Jaccard, Dice, and Cosine—for PHFSs and introduce layered set-based similarity measures (LSSMs), which externally integrate distinct semantic components. The effectiveness of LSSM is demonstrated by extending the CRITIC technique for decision-making, allowing for the determination of criteria weights even when information is incomplete or uncertain. We further validate the proposed measures using Spearman's correlation coefficient to analyze ranking stability and context sensitivity, facilitating their selection for specific decision scenarios. Finally, we apply the developed measures to optimize electric vehicle charging infrastructure in a large-scale problem, showcasing their practical applicability.

This article studies optimal planning and operation of electric vehicle (EV) charging stations within power distribution networks, which is crucial due to the growing penetration of EVs and distributed energy resources. Traditional approaches for planning and operation can be suboptimal and lead to significant grid upgrade costs. To tackle this, we propose an integrated framework that jointly optimizes the location, sizing, and operation of battery energy storage systems and EV charging stations. Our multiobjective optimization model minimizes power losses, operational costs, and environmental impacts while maximizing system reliability. We use genetic algorithms and deep deterministic policy gradients to solve this problem, departing from conventional peak-demand-based designs and leveraging typical demand profiles for battery energy storage system sizing. Real-time controllers are incorporated to adjust charging rates dynamically, responding to real-time variations. The proposed framework, validated on realistic test networks, demonstrates improved efficiency and stability, supporting sustainable and resilient grid operations.

This article proposes a network hosting capacity-aware local energy trading (LET) among residential customers to reduce their electricity costs, while the margins of the distribution stakeholders, including a retailer and a network operator, are maximized. A maximized customer export and import approach is proposed using dynamic operating envelopes. With maximized export and import allocation, a cooperative local energy market paradigm is proposed, in which customers, a retailer, and a network operator engage in peer-to-peer energy trading to reap the maximum possible financial benefits. The mathematical properties of the proposed LET paradigm, which include cooperation benefit, cooperation stability, pricing stability, and allocation fairness, are also proven to articulate the framework generality. A network hosting capacity-aware LET algorithm is also provided and deployed on a representative low-voltage distribution network. The performances of the proposed algorithm in terms of the integrity of the distribution network, the reduction in electricity cost reduction for customers, and the increase in margins of stakeholders are validated by extensive simulation results.

Multi-view classification (MVC) has emerged as a promising approach in machine learning, aimed at enhancing classification accuracy by leveraging information from multiple perspectives. As the demand for more robust, interpretable, and effective machine learning models grows, MVC has shown significant progress over the past decade, yet it faces new challenges. Despite extensive literature on this subject, there is a notable absence of a comprehensive synthesis of MVC methods. This article addresses this gap by presenting a thorough overview and classification of MVC methods, categorizing them into seven distinct classes: text, image, time series, hyperspectral, video, signal, and 3D shape. Our meticulous examination within each class highlights advancements and evaluates their applicability in both supervised and semi-supervised learning contexts. Beyond this retrospective analysis, we explore future directions for research and development in this domain. This survey serves as a compendium of existing knowledge and as a guide for future endeavors in MVC, shaping the trajectory of ongoing research and innovation.

Attributed graph clustering is a fundamental task in network mining, essential for uncovering valuable insights in various applications. However, the heterogeneity of information from structural and attribute spaces poses significant challenges in achieving consistent and meaningful clustering. To address this, we propose Relative Entropy-based Regularized Non-negative Matrix Factorization (RENMF), a novel approach that integrates structural and attribute information through advanced matrix factorization techniques. RENMF employs Symmetric NMF and Projective NMF to extract community membership distributions from the structural and attribute spaces, respectively. By treating these distributions as homogeneous, RENMF preserves distinct, denoised information from both spaces while considering their heterogeneous complementary information. We introduce Relative Entropy (RE) as a novel regularization term to facilitate interaction between these spaces, aiming to maximize consistency between the discovered latent distributions. In this interaction, we leverage the asymmetric property of RE to emphasize attributes as essential complementary information for structural clustering. The RENMF model is solved using a new iterative multiplicative update rule, with convergence theoretically proven. We evaluate RENMF’s effectiveness through extensive experiments on 10 real-world networks, comparing it to 11 state-of-the-art clustering methods. The results demonstrate RENMF’s superiority in ground truth matching and key quality metrics, outperforming existing methods.

The point-to-point Multi-objective Shortest Path (MOSP) problem is a classic yet challenging task that involves finding all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies have shown that employing A* search can lead to state-of-the-art performance in solving MOSP instances with non-negative costs. This paper proposes a novel A*-based multi-objective search framework that not only handles graphs with negative costs and even negative cycles but also incorporates multiple speed-up techniques to enhance the efficiency of exhaustive search with A*. Through extensive experiments, our algorithm demonstrates remarkable success in solving difficult MOSP instances, outperforming leading solutions by several factors.

This paper introduces an explainable Graph Neural Network (GNN)-based recommender system that integrates user-item interactions and user reviews to enhance recommendation accuracy and interpretability. The proposed method leverages Temporal Convolutional Networks (TCNs) as a language model to encode user reviews into vector representations, capturing temporal dynamics and contextual information. Additionally, it extracts opinion-aspect pairs from reviews, enabling the system to understand specific product features and user sentiments. Bipartite graphs are constructed to represent interactions between users/items and opinion aspects, facilitating the integration of user reviews into the GNN framework. A contrastive learning approach is employed to combine these graphs with TCN-generated review embeddings, enhancing the system's ability to capture complex relationships. Finally, a recommendation strategy is proposed which considers relevant opinion-aspects as explanations for recommendations. The experiments conducted on several benchmarks reveal that our method outperforms its competitors.

This paper focuses on identifying the most influential distributed generators (DGs) to improve the control performance of islanded microgrids with switching communication networks. Based on the secondary control scheme, frequency regulation and active power sharing are achieved in microgrids by pinning only a fraction of DGs. To improve the dynamic response of microgrids under limited control resources, a method is proposed to identify the most influential DG set that guarantees the fastest synchronization speed. By analyzing the impacts of the switching frequency of communication networks, an effective threshold is calculated to ensure performance. Furthermore, the proposed results are tested on a modified IEEE 34-bus system to evaluate the performance.

The increasing installation of Photovoltaics (PV) cells leads to more generation of renewable energy sources (RES), but results in increased uncertainties of energy scheduling. Predicting PV power generation is important for energy management and dispatch optimization in smart grid. However, the PV power generation data is often collected across different types of customers (e.g., residential, agricultural, industrial, and commercial) while the customer information is always de-identified. This often results in a forecasting model trained with all PV power generation data, allowing the predictor to learn various patterns through intra-model self-learning, instead of constructing a separate predictor for each customer type. In this paper, we propose a clustering-based multitasking deep neural network (CM-DNN) framework for PV power generation prediction. K-means is applied to cluster the data into different customer types. For each type, a deep neural network (DNN) is employed and trained until the accuracy cannot be improved. Subsequently, for a specified customer type (i.e., the target task), inter-model knowledge transfer is conducted to enhance its training accuracy. During this process, source task selection is designed to choose the optimal subset of tasks (excluding the target customer), and each selected source task uses a coefficient to determine the amount of DNN model knowledge (weights and biases) transferred to the aimed prediction task. The proposed CM-DNN is tested on a real-world PV power generation dataset and its superiority is demonstrated by comparing the prediction performance on training the dataset with a single model without clustering.

Electricity smart meters have been widely adopted primarily for billing purposes. These meters provide a net-metering of the residential unit without distinguishing between the energy consumption or generation. As the penetration of distributed solar photovoltaic is expected to account for at least one-fourth of the energy mix in 2050, the visibility of the distributed energy resources to distribution network operators is vital. The disaggregation of behind-themeter net metering estimates the energy consumption and generation of residential units which enhances the observability of the low voltage network and provides analytics to support decision making. In this paper, we train a feedforward artificial neural network (ANN) to disaggregate the net metering data into energy consumption and generation. This model can be integrated as part of the distribution network operator tools for supporting decision making. The results show that ANN can disaggregate energy consumption and generation with an average RMSE and MAE performance of 0.1 when applied on real datasets.

Abstract The classic problem of constrained pathfinding is a well‐studied, yet challenging, network optimization problem with a broad range of applications in various areas such as communication and transportation. The weight constrained shortest path problem (WCSPP), the base form of constrained pathfinding with only one side constraint, aims to plan a cost‐optimum path with limited weight/resource usage. Given the bi‐criteria nature of the problem (i.e., dealing with the cost and weight of paths), methods addressing the WCSPP have some common properties with bi‐objective search. This article leverages the recent state‐of‐the‐art techniques in both constrained pathfinding and bi‐objective search and presents two new solution approaches to the WCSPP on the basis of A* search, both capable of solving hard WCSPP instances on very large graphs. We empirically evaluate the performance of our algorithms on a set of large and realistic problem instances and show their advantages over the state‐of‐the‐art algorithms in both time and space metrics. This article also investigates the importance of priority queues in constrained search with A*. We show with extensive experiments on both realistic and randomized graphs how bucket‐based queues without tie‐breaking can effectively improve the algorithmic performance of exhaustive A*‐based bi‐criteria searches.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

Energy demand prediction can be obtained for different customer categories or geolocations, e.g., predicting the energy demand over different cities. Traditionally, these prediction tasks are solved independently without considering the common problem-solving knowledge sharing among them. However, addressing one task may help facilitate the training process or improve the prediction performance of another one via knowledge transfer. In this article, we propose a two-stage multitasking prediction (TS-MTP) framework to address the energy demand prediction problem over multiple locations, in which each task has a deep neural network (DNN) model as the predictor. TS-MTP includes single-tasking learning (STL) and multitasking learning (MTL) stages. The STL stage focuses on addressing each prediction task independently with a gradient descent-based optimization algorithm until the training accuracy cannot be improved, so that the optimal DNN structure parameters for each task can be achieved. In the MTL stage, for a specified target task, the knowledge, i.e., DNN connection weights and biases acquired in STL, is extracted and transferred from the source tasks and reused in the target task to help further improve its prediction accuracy. To decide the amount of knowledge to be reused, a coefficient is assigned to each source task, and particle swarm optimization is applied to obtain the optimal coefficients. The performance of TS-MTP is verified on several problem sets that are created from different step-ahead predictions. The superiority of TS-MTP is demonstrated in comparison to several state-of-the-art DNNs that are popular in the time-series prediction domain. The results show that TS-MTP can lead to a more than 35% accuracy improvement compared with the STL without knowledge transfer.

The Electric Vehicle (EV) fleet's growth presents opportunities and challenges for the power grid, from reduced emissions and renewable energy integration to unprecedented increases in electricity peak demand. Strategic planning and incentive policies can help ensure a smooth integration of EVs into the power grid, ensuring grid reliability while reaping the associated benefits. Addressing the challenges proactively and leveraging the opportunities presented by the growing EV fleet requires collaboration among various stakeholders, including prosumers, EV Charging Station (EVCS) owners, and utilities. This paper proposes a multi-objective optimization framework considering battery degradation cost, Photovoltaic (PV) build canopy EVCS, and cost reduction programs to reduce the load variance and prosumer cost, minimize power losses, and maximize EVCS benefits. Furthermore, we propose a carbon credit program to compensate EV owners' battery degradation costs and encourage them to participate in the Vehicle-to-Grid (V2G) program. The sensitivity analysis results indicate that the proposed method effectively impacts objective functions and load indices.

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed ε-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods.

Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the “Laplacian energy” of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.

Electric vehicles (EVs) often consume large amounts of energy, and uncoordinated charging of many EVs may lead to grid overload, adversely impacting other customers. Electricity distributors require full visibility on the EV distribution to better manage operation planning of their distribution grid. However, they often have incomplete knowledge of EV presence in their network. Identifying EV customers (charging at home) using smart meter data is a nontrivial task for the grid network and energy scheduling. The difficulties include recognizing charging patterns, balancing the number of EV and non-EV customers during modeling, and building an efficient classification model. In this article, we propose a periodic pattern recognition method to extract useful EV charging patterns. Real world smart meter datasets are unbalanced with few EVs and majority of energy customers are those without EVs. We improve Kmedoids evaluated by dynamic time warping to obtain the representative non-EV training samples so that balanced samples over EV and non-EV customers can be obtained. We develop an ensemble classification model (ECM) by taking advantages of multiple classifiers, in which the optimization consists of obtaining the optimal subset of periodic patterns and the optimal parameters in each classifier and the optimal weights for combining classifiers. The superiority of the proposed ECM is demonstrated in comparison to several baseline models.

Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance. In this work, a novel representation learning approach is introduced based on spectral convolutions on graph-structured data in a semisupervised learning setting. Our proposed method, COnvOlving cLiques (COOL), is constructed as a neighborhood aggregation approach for learning node representations using established GCN architectures. This approach relies on aggregating local information by finding maximal cliques. Unlike the existing graph neural networks which follow a traditional neighborhood averaging scheme, COOL allows for aggregation of densely connected neighboring nodes of potentially differing locality. This leads to substantial improvements on multiple transductive node classification tasks.

In this paper, control of Battery Storage Systems (BSS) in power distribution grids with residential consumers as well as prosumers equipped with rooftop photovoltaic (PV) solar panels and Electric Vehicles (EV) is addressed. Different features of these Distributed Energy Resources (DERs), such as intermittent behaviour and the difference between the maximum generation time and the maximum demand, have caused several issues for electricity distributors in delivering high quality power. Smart control and scheduling of ESS and EVs is a promising approach to protect the grid against extra power injection from prosumers during day times while the benefit of household owners from DERs are still achieved. In this context, the performance of model-based controllers such as model predictive controllers (MPC) is compared with model-free data driven controllers (DDC) considering different complex scenarios that may happen in a distribution grid. The control objective is to minimize the difference between the net power exchanged with the main grid from the estimated average net load of prosumers. Our study on the real consumption data of about 40 residential consumers/prosumers in Victoria, Australia, demonstrates the strength of data-driven control approaches to deal with the complex environment of power distribution grids in the presence of DERs.

In this paper, we propose a simple cohesive framework to find an optimal directed control network topology with minimum number of links while a prescribed decay rate is satisfied in the transient response of a distributed control system. In order to guarantee the system's decay rate to be faster than a prespecified value, a constraint on the dominant eigenvalue of the system is required to be considered. This results in a nonconvex optimization problem as eigenvalue of a parametric nonsymmetric matrix is a nonconvex, nonsmooth, and even non-Lipschitz function. Here, we present a convex equivalent optimization problem whose minimizer also solves this eigenvalue optimization problem. This optimization problem proposes a state-feedback matrix which results in a decay rate faster than a given value while input signal costs are considered. The equivalent optimization problem in combination with sparsity-promoting optimal control constitutes a combinatorial optimization problem. Using alternating direction method of multipliers, the problem is decomposed into a chain of analytically solvable subproblems which are differentiable and separable. The proposed optimization framework includes relative preference between the topology of the control network and the decay rate of the system. The simulation results show the effectiveness of the proposed framework.

Increased deployment of residential smart meters has made it possible to record energy consumption data on short intervals. These data, if used efficiently, carry valuable information for managing power demand and increasing energy consumption efficiency. An efficient way to analyze these data is to first identify clusters of energy consumers, and then focus on analyzing these clusters. However deciding on the optimal number of clusters is a challenging task. In this article, we propose a clustering index that effectively finds the optimal number of clusters. The proposed index is an entropy-based measure that is obtained from eigenvalue analysis of the correlation matrix of time series of consumption data. A genetic algorithm based feature selection is used to reduce the number of features, which are then fed into clustering algorithms. We apply the proposed clustering index on two ground truth synthetic data sets and two real world energy consumption data set. The numerical simulations reveal the effectiveness of the proposed method and its superiority to a number of existing clustering indices.

Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods.

SUMMARY: Computational metabolic models typically encode for graphs of species, reactions and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model. AVAILABILITY AND IMPLEMENTATION: The GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor-a public version is available at sbi.uni-rostock.de/gemtractor.

Faster convergence is always sought in many applications. Designing fixed-time control has recently gained much attention since, for this type of control structure, the convergence time of the states does not depend on initial conditions, unlike other control methods providing faster convergence. This paper proposes a new distributed algorithm for second-order consensus in multiagent systems by using a full-order fixed-time convergent sliding surface. The stability analysis is performed using the Lyapunov function and bi-homogenous property. Moreover, the proposed control is smooth and free from any singularity. The robustness of the proposed scheme is verified both in the presence of Lipschitz disturbances and uncertainties in the network. The proposed method is compared with a state-of-the-art method to show the effectiveness.

The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradually involving easy to hard tags into the learning process. Besides, the utilization of a diversity maintenance approach avoids overfitting on a subset of easy labels. In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.

Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods.

Recommender systems aim to suggest relevant items to users among a large number of available items. They have been successfully applied in various industries, such as e-commerce, education and digital health. On the other hand, clustering approaches can help the recommender systems to group users into appropriate clusters, which are considered as neighborhoods in prediction process. Although it is a fact that preferences of users vary over time, traditional clustering approaches fail to consider this important factor. To address this problem, a social recommender system is proposed in this paper, which is based on a temporal clustering approach. Specifically, the temporal information of ratings provided by users on items and also social information among the users are considered in the proposed method. Experimental results on a benchmark dataset show that the quality of recommendations based on the proposed method is significantly higher than the state-of-the-art methods in terms of both accuracy and coverage metrics.

Demand response has been used as a technique to influence the behavior of energy consumers to reduce their energy consumption based on participation in incentive-based or event-driven programs. In this paper, a Markov Decision Process is proposed as a decision making framework to study the behavior of energy consumers under different energy pricing policies utilizing rewards and/or penalties. Numerical results show that a combination of both rewards and penalties in the energy pricing policy offer the ideal reduction in power demand averaged over 30 minutes during high peak period.

This paper addresses the problem of finding the most synchrony vulnerable node in complex networks, i.e. the node which removal has the maximum influence on synchronizability of the network. In large-scale networks, brute search techniques are often not computationally cost effective in identifying the most vulnerable node(s). Here, considering the eigenratio of the Laplacian matrix of a graph as the synchronizability metric, we propose a measure in order to approximately rank nodes based on their impact on the synchronizability. This metric is cost effective since it needs a single eigen-decomposition of the Laplacian matrix of the connection graph. Simulation results show that the proposed metric is accurate enough in predicting the most vulnerable node in synthetic networks with scale-free, Watts-Strogatz and Erdös-Rényi structures.

This paper describes pinning synchronization of a complex dynamical network consisting of N identical nodes. The nodes are interconnected by a time-varying non-linear coupling terms, which has a general type with some constraints. Many non-linear coupling forms can be modeled as the one considered in this work. The network synchronization is achieved by using non-linear switching control. The stability of the synchronization is proven mathematically using Lyapunov analysis. It is shown that the proposed controller performs well in the presence of disturbances. Finally, simulation examples of Lorenz oscillator networks are given to verify the theoretical results. The simulations show that the proposed switching control outperforms classical linear control by providing not only faster synchronization, but also better robustness against external disturbances.

One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the sign prediction problem. The main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network. Building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works. We suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma. To this end, we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing. The model relies more on the global structures when the sparsity increases, whereas it gives more weights to the information obtained from local structures for low levels of the sparsity. The proposed model is assessed on three real-world signed networks, and the experiments reveal its consistent superiority over the state of the art methods. As compared to the previous methods, the proposed model not only better handles the sparsity problem, but also has lower computational complexity and can be updated using real-time data streams.

In this paper, the stability problem of formation of multi-agents subject to failures in their communication links is addressed. The objective of the formation control problem is to maintain the inter-agent distances to be constants over time using a distributed control algorithm implemented in each agent. Previous research results showed that a distributed gradient-based control can locally asymptotically stabilize an undirected formation. However, in the case of failures in the inter-agent communication network, the degrees of freedom for some nodes might become uncontrollable and, consequently, the formation starts deviating from the desired conditions due to uncertainties and noise. In this paper, it is proved that in a faulty formation system, if there still exists a path between the agents on the both sides of the failed link, the gradient-based control signal can recover the formation without adding any new link to the network. Based on this feature, an algorithm for recovering the formation from the fault is developed. Simulation results show that the proposed recovery algorithm can tolerate small values of delays in data communications.

In this paper, we propose an optimization methodology to find the optimal topology for the data communication network in distributed frequency control of power system. In order to implement a distributed cooperative control scheme, local controllers often share their data over a data communication network. Structure of this network has a major role in determining stability of secondary cooperative frequency control of a microgrid; and thus the structure can be optimized to have the best performance. Although distributed control signals may be delayed or dropped during their transmission, confident margin of the stability can reduce side effects of these inherent problems and makes power system more reliable. In this situation, the challenge is to find the best topology of data communication network giving the highest margin of the system stability. In this paper we define the problem of finding the best topology as an optimization problem. An eigenvalue perturbation analysis approach is used to approximate sensitivity of the stability performance of secondary cooperative control to adding/removing data communication links. Then, the optimization problem is solved using a simulated annealing optimization strategy. Our numerical simulations on sample networks show that the proposed rewiring-based optimization can successfully find the network structure with (near)-optimal stabilizability performance.

Cascading failure in power grids might lead to a catastrophic black out in power systems. One resolution to deter the power grid from failure is load shedding. There are different types of load shedding in the literature which mostly cut off some loads from the grid to preserve the rest of the network's connectivity and functionality. However sometimes it is not the case and disconnecting some feeders from the power grid is impractical due to their vitality for the economy or the society. In this paper, as soon as a transmission line gets overloaded due to any rise in overall load, since its breakdown can trigger a cascade of failures, we propose a method to rank the loads to be reduced in order to prevent that particular line from failure. After the loads are ranked, the top ranked load will be a candidate to get reduced. If this action wasn't enough or feasible, the second top ranked load is chosen and so on. Even a set of loads with different percentage of load reduction can be picked. As the pre-failure data, we apply the results from load flow analysis or the last working state of the power grid. These data is applied to calculate the specific transmission line's sensitivity to changes in different loads in power grids. The results show that this method is much more efficient when the classic methods suffer from divergence and setting malfunctioning.

This paper presents the cascaded PI-continuous second order sliding mode control for induction motor in the presence of operational constraints. The inner-loop Sliding Mode Control (SMC) is designed to control the current dynamics of the motor while the outer-loop control is the PI control of speed. The main advantages of the proposed method are that the PI control provides reference to inner-loop SMC with constraints according to the system requirements in terms of maximum current and speed limits. Moreover the inner-loop dynamics of the motor being more non-linear, SMC design has more importance in terms of robustness and disturbance rejection capability. The proposed cascade PI with SMC is chattering-free control action with fixed-time convergence. The performance of the developed controller is validated and compared by carrying out real-time experimental studies. Experimental results demonstrate remarkable robust tracking performance of the controller in terms of transient response speed and steady-state accuracy.

Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05). The AD brains have significantly higher local connectivity than healthy controls in alpha and beta bands. This decreased profile in global connectivity can be linked to compensatory increased local connectivity as a result of wide-spread decline in the long-range connections. We also study resiliency of brain networks against targeted attack to hub nodes and find that AD networks are less resilient than healthy brains in alpha and beta bands.

In this paper we study stability of a power system consisting distributed generation units. A complete model for the power grid including dynamics of loads as well as interchange of power is taken into account. A cooperative secondary control scheme takes into account internal dynamic of each node and is implemented to improve the stability of the system. In this paper, a condition for the stability of power grid in the presence of communication network is achieved. We support the results by numerical simulations on various connection topologies including scale-free and random structures. We find that increasing the number of communication links does not generally improve the stability of the whole power network.

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

This manuscript discusses a new algorithm for finite time convergence of the states for a higher order system. To this end, a set of fast terminal sliding mode surfaces are constructed. The fractional powers of the switching surfaces are selected such that singularity is avoided during sliding. A control signal is developed with necessary and sufficient conditions for the finite time convergence is obtained, under which the system states reach the fast terminal sliding surfaces in a finite time and stay in the sliding modes thereafter. A condition on external disturbance is obtained to guarantee the finite time convergence. Numerical simulations are provided to validate the theoretical results and examine the robustness of control against external disturbances.

In this paper we introduce a novel population-based binary optimization technique, which works based on consensus of interacting multi-agent systems. The agents, each associated with an opinion vector, are connected through a network. They can influence each other, and thus their opinions can be updated. The agents work collectively with their neighbors to solve an optimization task. Here we consider a specific opinion update rule and various topologies for the connection network. Our experiments on a number of benchmark non-convex cost functions show that ring topology results in the best performance as compared to others. We also compare the performance of the proposed method with a number of well-known optimizers (genetic algorithms, binary particle swarm optimizer, and binary differential evolution) and show its outperformance over them. The proposed optimizer also shows rather fast convergence to the optimal solution.

Recommender systems have been accompanied by many applications in both academia and industry. Among different algorithms used to construct a recommender system, collaborative filtering methods have attracted much attention and been used in many commercial applications. Incorporating the time into the recommendation algorithm can greatly enhance its performance. In this paper, we propose a novel time-aware model-based recommendation system. We show that future ratings of a user can be inferred from his/her rating history. We assume that there is cascade of information between the items such that rating an item can lead to other items being rated. There is indeed a hidden network structure among the items and each user tracks a sequence of items in this network. The dependencies between the items are modeled based on statistical diffusion models and the parameters are obtained through maximum-likelihood estimation. We show that under some mild assumptions, the estimation task becomes a convex optimization problem. A major advantage of the proposed method over classical recommender systems is the ability to include novel items in its recommendation lists besides providing accurate recommendations. The proposed model also results in personalized and diverse recommendations. Experimental evaluations show that the model can be trained based on the ratings of a limited number of users. Furthermore, the proposed model outperforms classical recommendation algorithms in terms of both accuracy and novelty.

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataâthat are freely availableâgive information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions.

Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.

In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta band of AD subjects shows an increase in the power, whereas the alpha band shows a decreased activity (P-value <0.05). In the right brain hemisphere of AD subjects, a decreased activity is observed in all frequency bands. It was also noticed that the right temporal region shows a significant difference between the two groups in all frequency bands. Using a support vector machine, control and patient groups are discriminated with an accuracy of 84.4%, sensitivity 75.0%, and specificity of 93.7%.

In this paper, we review the literature on enhancing synchronizability of diffusively coupled dynamical networks with identical nodes. The last decade has witnessed intensive investigations on the collective behavior over complex networks and synchronization of dynamical systems is the most common form of collective behavior. For many applications, it is desired that the synchronizability-the ability of networks in synchronizing activity of their individual dynamical units-is enhanced. There are a number of methods for improving the synchronization properties of dynamical networks through structural perturbation. In this paper, we survey such methods including adding/removing nodes and/or edges, rewiring the links, and graph weighting. These methods often try to enhance the synchronizability through minimizing the eigenratio of the Laplacian matrix of the connection graph-a synchronizability measure based on the master-stability-function formalism. We also assess the performance of the methods by numerical simulations on a number of real-world networks as well as those generated through models such as preferential attachment, Watts-Strogatz, and Erdos-Rényi.

Synchronizability of dynamical networks can be defined as the ease by which the network synchronizes its activity. We propose a method for enhancing the synchronizability of dynamical networks by efficient rewirings. The method is based on the eigenvectors corresponding to the second smallest and the largest eigenvalue of the Laplacian matrix and a modified version of the simulated annealing approach is used to perform the optimization task. Starting from a simple network, i.e. an undirected and unweighted network, and at each step, an edge is selected for disconnection and two non-adjacent nodes for creating at edge in between. The effectiveness of the algorithm is tested on artificially constructed networks such as random, Watts-Strogatz, and scale-free ones. We also investigate the coincidence of two measures of synchronizability, i.e. the eigen-ratio of the Laplacian matrix and the cost of synchronization, in the optimized networks.

In this paper we investigate optimization of synchronization cost in undirected dynamical networks. To do so, proper weights are assigned to the networks' edges considering node and edge betweenness centrality measures. The proposed method gives near-optimal results with less complexity of computation compared to the optimal method, i.e. the method based on convex optimization. This property enables us to apply the method to large networks. Through numerical simulations on scale-free and small-world networks of different size and topological properties we give evidence that the performance of the proposed method is much better than another heuristic method; namely the Metropolis-Hasting algorithm. This procedure has potential application in many engineering problems where the synchronization of the network is required to be achieved by minimal cost.

In this paper, using the mathematical properties of self-kronecker-production of small size random matrices, a simple but effective method is presented to optimize the reservoir of an echo state network given a certain task. The experimental results investigating the NARMA system show that few steps of the proposed optimization process can lead to a near optimum solution.

The paper presents an intelligent predictive control to govern the dynamics of a solar power plant system. This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neuro-fuzzy predictive control, can be a better match to govern the system dynamics. In our proposed method, a neuro-fuzzy model identifies the future behavior of the systems over a certain prediction horizon while an optimizer algorithm based on EP determines the input sequence. The first value of this sequence is applied to the plant. Using the proposed intelligent predictive controller, the performance of outlet temperature tracking problem in a solar power plant is investigated. Simulation results demonstrate the effectiveness and superiority of the proposed approach.

This paper, presents a traction control algorithm for an electric vehicle (EV) with four separate wheel drives. This algorithm is necessary to improve the handling and stability of EV during cornering or under slippery road condition. It distributes the traction power among four drives and especially an independent torque reference on each wheel. The proposed algorithm is implemented in terms of a hierarchical architecture, which incorporates all new known vehicle systems ABS (anti-lock brake system), ASR (anti slip regulation), ESP (electronic stability program). To achieve good performances, a robust sliding mode control strategy is implemented. Several simulation results, which show the potential of such an algorithm, are presented.

The paper presents the design of fuzzy sliding mode control based on genetic algorithms. An optimal design criterion is not required in the design of a fuzzy sliding mode control. Therefore, we design a fuzzy sliding mode controller for the general nonlinear control systems as an optimization problem and apply the optimal searching algorithms and genetic algorithms to find the optimal rules and membership functions of the controller. The proposed approach has the merit to determine the optimal structure and the inference rules of fuzzy siding mode controller simultaneously. Using the proposed approach, the tracking problem of two-degree-of-freedom rigid robot manipulator is studied. Simulation results of the close-loop system with the proposed controller based on genetic algorithms show the effectiveness of the approach.

Acquisition and utilization of lateral guidance is crucial for steering a vehicle. In practice, human drivers have performed this function quite successfully using perception and hand-eye coordination. However, this task becomes difficult when the vehicle losses its stability. In this paper, we try to investigate this problem using predictive control strategy. The most important advantage of the MPC technology comes from the process model itself, which allows the controller to deal with an exact replica of the real process dynamics, implying a much better control quality. Simulation results of the closed-loop system with proposed predictive control algorithm reveal the effectiveness of the proposed control action in controlling the steering of the vehicle.