Accurate travel time estimation (TTE) plays a crucial role in intelligent transportation systems. However, it remains challenging due to heterogeneous data sources and complex traffic dynamics. Moreover, traditional approaches typically convert trajectory data into fixed-length representations. This overlooks the inherent variability of real-world motion patterns, often resulting in information loss and redundancy. To address these challenges, this paper introduces the Multimodal Dynamic Trajectory Integration (MDTI) framework--a novel multimodal trajectory representation learning approach that integrates GPS sequences, grid trajectories, and road network constraints to enhance the performance of TTE. MDTI employs modality-specific encoders and a multimodal fusion module to capture complementary spatial, temporal, and topological semantics, while a dynamic trajectory modeling mechanism adaptively regulates information density for trajectories of varying lengths. Two self-supervised pretraining objectives, named contrastive alignment and masked language modeling, further strengthen multimodal consistency and contextual understanding. Extensive experiments on three real-world datasets demonstrate that MDTI consistently outperforms state-of-the-art baselines, confirming its robustness and strong generalization abilities. The code is publicly available at: https://github.com/City-Computing/MDTI.

Cross-domain sequential recommendation is increasingly important in modern Web ecosystems, where user behaviors span multiple independently operated services that maintain strict data isolation for privacy and regulatory compliance. Federated learning offers a practical paradigm for such cross-domain collaboration, but user preferences evolve asynchronously across services, creating a substantial distribution shift. This drift leads to unstable and unequal domain contributions: behaviorally rich domains dominate global updates, while low-resource or volatile domains exert limited influence. Such an imbalance degrades recommendation accuracy and raises fundamental fairness concerns. To address these challenges, we propose FairFRL, a fairness-aware federated representation learning framework designed to mitigate contribution imbalance under dynamic cross-domain drift. FairFRL mitigates contribution imbalance under dynamic cross-domain drift by jointly regulating domain influence during federated aggregation and disentangling domain-shared and domain-exclusive semantics, while preserving data locality. Experiments on real-world Amazon multi-domain datasets show that FairFRL consistently outperforms strong federated and centralized baselines across multiple metrics and achieves more equitable cross-domain contributions. These results position FairFRL as a principled step toward responsible, fair, and socially aligned Web recommendation systems.

Cross-domain sequential recommendation (CDSR) plays a critical role in decentralized Web applications by leveraging user behavior sequences across multiple platforms to alleviate data sparsity and capture dynamic preferences. However, existing federated CDSR frameworks face two fundamental challenges: (i) heterogeneous sequential interactions that encode domain-exclusive semantics and cannot be directly shared under privacy constraints, and (ii) strong trust assumptions that both servers and clients behave honestly, leaving federated training vulnerable to misreporting, malicious updates, and negative transfer. In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. VeriFRL adopts a dual-module design that integrates representation learning with verifiable training: an attention-based variational encoder disentangles domain-shared and domain-exclusive representations to support transferable and privacy-preserving knowledge sharing, while a contribution evaluation module quantifies client-level and feature-level influences to enable verifiability, interpretability, and negative transfer detection. Extensive experiments on real-world multi-domain datasets demonstrate that VeriFRL achieves competitive or superior recommendation performance over state-of-the-art federated CDSR methods, while providing fine-grained insights into cross-domain knowledge transfer dynamics.

Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual History-aware HyperGraph Network for TKG reasoning, namely DHHGN. Specifically, for local history modeling, we design a hybrid hypergraph-graph joint recurrent convolution module that simultaneously captures low-order neighborhood information and high-order interaction patterns among entities, employing a gating mechanism to adaptively blend their contributions. For global history modeling, we propose a dual history enhancement module that amplifies attention on pivotal historical facts while ensuring holistic integration of all historical contexts. Extensive experiments on four public benchmarks validate that DualHist-HGN consistently outperforms existing state-of-the-art methods across TKG reasoning tasks.

Temporal Knowledge Graph Completion (TKGC) aims to predict missing entities or relations based on historical facts, thereby facilitating the understanding of dynamic system evolution and supporting downstream reasoning tasks. However, existing methods predominantly focus on modeling sequential and structural dependencies, often overlooking the rich semantic information embedded in entities and relations, as well as the higher-order interactions among them, which limits their ability to handle complex, evolving scenarios effectively. To address these limitations, we propose DSTAG, a novel TKGC approach based on a semantic tag-enhanced dual-graph convolutional network. Our method leverages large language models to generate contextualized semantic multi-tags for both entities and relations (e.g., ''political event,'' ''economic activity''), thereby enriching their semantic representations. Furthermore, we introduce a semantic tag representation mechanism that captures higher-order dependencies during the aggregation and propagation of semantic tag information across graphs. DSTAG adopts a dual-graph convolutional network architecture, where the relation graph convolution extracts semantic features between temporal relationships and injects this information into the entity graph convolution, enabling joint modeling of entities and relations. We evaluate DSTAG on three widely used TKG benchmarks: ICEWS14, ICEWS18, and ICEWS05-15. Experimental results show that DSTAG achieves substantial MRR improvements over state-of-the-art baselines by 8.64%, 9.81% and 4.56%, respectively.

The Transformer model, with its ability to capture long-term dependencies, has demonstrated significant potential in enhancing long-term traffic flow prediction for effective urban transportation management. However, most existing Transformer-based methods adhere to a centralized approach, failing to address privacy concerns and to optimize computational resource utilization. Although emerging federated learning paradigms offer privacy protection, the average aggregation still overlooks client heterogeneity and lacks the synchronous efficiency required for traffic flow prediction tasks. Consequently, we introduce FedTFormer, a hierarchical federated learning framework tailored to boost the performance of Transformer models in decentralized environments, involving clients, edge servers, and a central server. Initially, clients are organized into clusters through a sophisticated static clustering mechanism anchored in bipartite graph theory. FedTFormer enhances robustness of Transformer by facilitating synchronous average aggregation within clusters. Additionally, it performs asynchronous fine-tuning of cluster-specific parameters, leveraging hypernetwork constructed on the central server. Clients utilize an optimized Transformer model for localized training, harnessing its proficiency in capturing long-term spatio-temporal dependencies. Ultimately, we conduct extensive experiments across three datasets, comparing our method against ten sophisticated approaches and demonstrating the effectiveness and robustness of FedTFormer.

Federated Graph Learning (FGL) has emerged as a key paradigm for distributed graph machine learning, enabling cross-domain graph collaborative modeling while preserving data privacy. However, existing methods face two major bottlenecks: the structural heterogeneity discrepancy of graph data among clients weakens the generalization ability of the global model; and model heterogeneity leads to inefficient knowledge sharing and complex global aggregation. To address these issues, we propose FedGVD, an efficient framework that constructs a global perspective through data condensation and server-side virtual node generation, which not only preserves the semantic equivalence of the original data but also avoids privacy leakage. Subsequently, by distributing low-dimensional generalizable knowledge for unidirectional distillation, FedGVD enables local models to absorb global knowledge without transmitting local parameters, thus breaking through the challenges of data and structural heterogeneity as well as model heterogeneity. This innovative approach ensures privacy-preserving and efficient federated graph collaboration. Experiments show that FedGVD maintains excellent performance in heterogeneous model scenarios while significantly improving communication efficiency, offering a new approach for privacy-preserving collaborative modeling in FGL. The code is available at https://github.com/Jasonxx4/FedGVD.

Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can predict user preferences and demands, they fall short in generating explicit recommendation paths and lack explainability. As a step beyond the above methods, recent advancements utilize reinforcement learning (RL) to find suitable items for a given user via explainable recommendation paths. However, the performance of these solutions is still limited by the following two points. (1) Lack of ability to capture contextual dependencies from neighboring information. (2) The excessive reliance on short recommendation paths due to efficiency concerns. To surmount these challenges, we propose a category-aware dual-agent reinforcement learning (CADRL) model for explainable recommendations over KGs. Specifically, our model comprises two components: (1) a category-aware gated graph neural network that jointly captures context-aware item representations from neighboring entities and categories, and (2) a dual-agent RL framework where two agents efficiently traverse long paths to search for suitable items. Finally, experimental results show that CADRL outperforms state-of-the-art models in terms of both effectiveness and efficiency on large-scale datasets.

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge-such as the progression of social network interactions and the unfolding of news events-is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity's relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks.

Abstract Trajectories serve as a cornerstone of intelligent transportation systems, playing an important role in many applications such as traffic flow prediction, route planning, and urban management. However, the availability of such data is limited due to privacy issues, ethical concerns, and the high cost associated with infrastructure deployment. In recent years, rapidly developing generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs) have demonstrated strong capabilities in learning complex data distributions and generating synthetic data, thereby alleviating the data accessibility issue. In this survey, we systematically review the existing literature on deep generative models that address the problem of trajectory generation. First, we classify the existing literature into two categories: unconditional and conditional trajectory generation. In unconditional generation, trajectories are generated without contextual constraints, whereas conditional generation incorporates several important factors such as road network topology, time of day, and user preferences to guide the trajectory generation process. Then, for each category, we further classify the literature into three methodological types, including VAEs, GANs, and DMs, and analyze how these models address key challenges under different settings. Finally, we discuss promising directions for future research and hope to inspire further advances in trajectory generation.

The primary objective of graph node anomaly detection is to pinpoint rare patterns that display marked deviations from the typical one. Existing methods utilize Graph Convolutional Networks (GCNs) to model complex interactions in Heterogeneous Information Networks (HINs), typically homogenizing HINs using meta-paths to effectively focus on particular semantic scenarios. However, meta-paths excessively emphasize specific nodes and their connections on predefined paths, leading to the neglect of one-hop context-rich neighbors. Furthermore, the conversion from heterogeneous to homogeneous structures disrupts inherent relationships, resulting in an irreversible loss of direct links. Thus, we propose a dual-view-based H eterogeneous I nformation N etworks Node Ano maly Detection framework, HINAno, to mitigate structural loss. HINAno adopts a synergetic approach that balances local structural information with semantic richness, drawing from both the one-hop neighbor view and the meta-path view. Specifically, this dual-view utilizes hierarchical fusion mechanisms at node, type, and semantic levels to capture one-hop and multi-hop neighborhoods in a level-wise manner. In addition, HINAno adopts self-supervised contrastive learning and GCNs to amplify the gap between normal and abnormal nodes, thereby reducing the reliance on anomalous labels and enhancing the capability of anomaly detection. Finally, we successfully verify that the HINAno framework is effective and superior on four real-world datasets.

Predicting traffic accidents can help traffic management departments respond to sudden traffic situations promptly, improve drivers’ vigilance, and reduce losses caused by traffic accidents. However, the causality of traffic accidents is complex and difficult to analyze. Most existing traffic accident prediction methods do not consider the dynamic spatio-temporal correlation of traffic data, which leads to unsatisfactory prediction accuracy. To address this issue, we propose a multi-task learning framework (TAP) based on the Spatio-temporal Variational Graph Auto-Encoders (ST-VGAE) for traffic accident profiling. We firstly capture the dynamic spatio-temporal correlation of traffic conditions through a spatio-temporal graph convolutional encoder and embed it as a low-latitude vector. Then, we use a multi-task learning scheme to combine external factors to generate the traffic accident profiling. Furthermore, we propose a traffic accident profiling application framework based on edge computing. This method increases the speed of calculation by offloading the calculation of traffic accident profiling to edge nodes. Finally, the experimental results on real datasets demonstrate that TAP outperforms other state-of-the-art baselines.

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.

Embedded systems are common in the Internet of Things domain: their integration in vehicles and mobile devices is being fostered in the Internet of vehicles (IoV). IoV has direct applications on intelligent transportation systems and smart cities. Besides basic requirements, such as ease of installation, cost-effectiveness, scalability, and flexibility, IOV applications need to guarantee energy-efficient and good-quality communication. In fact, IoV implementations are commonly based on wireless nodes, which rely on a limited energy source; therefore, an efficient communication among the nodes is desirable to prolong the lifetime of the devices. In particular, the alternation of active and sleep states and the regulation of the transmission power represent two common approaches to save energy. Based on this strategy, an effective fuzzy control system is presented in the paper to manage power consumption and quality of services of IoV applications. Two fuzzy controllers increase the battery life while keeping a good throughput to workload ratio. This technique has been simulated with two leading technologies in IoV: IEEE 802.11b/g/n and IEEE 802.11p. Experimental results show a network lifetime improvement ranging from 30% to 40%, according to the adopted medium access control protocol.

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars’ academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars’ academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars’ future success in the computer science area. Additionally, we discover an interesting phenomenon that the h -index of scholars within the same institution or university are actually very close to each other.