During the collision resolution phase, many deterministic RFID anti-collision protocols require tags to repeatedly transmit identical ID segments across multiple query cycles. This redundant transmission leads to high communication overhead, increasing identification latency and energy consumption, thereby constraining system performance and scalability. This paper proposes the Bitmap-Guided Collision Tree (BGCT) protocol to address this limitation. BGCT reduces tag transmissions through a progressive information acquisition mechanism, and enables multi-way collision resolution by integrating vector-based grouping with a bitmap. Theoretical analysis and simulation results demonstrate BGCT’s performance advantages. Specifically, for identifying 10000 tags(ID=96 bits), BGCT reduces the total identification time by 27.8% compared to the best competitor in this scenario, EMDT. For 256-bit IDs, assuming uniform or pseudo-random ID distributions, the progressive mechanism mitigates the dependency of communication cost on ID length, maintaining a low expected overhead in contrast to the linear growth observed in other protocols. Furthermore, under non-uniform ID distributions, BGCT demonstrates a collision resolution efficiency 152.9% higher than that of the competing protocols.

Satellite Edge Computing (SEC) enables intelligent onboard processing, which is essential for mission-critical tasks involving high data capture rates and compute-intensive Deep Neural Network (DNN) models. However, limited satellite computing capacity often causes significant processing delays, underscoring the need for efficient collaboration across the SEC network to ensure real-time responsiveness and optimal resource utilization. Since DNN models can be partitioned into sub-tasks executed in parallel across multiple nodes, partitioning naturally facilitates collaborative processing by exploiting idle resources and reducing transmission overhead, while also accelerating inference. Motivated by this, we propose HiO2, a hierarchical task offloading framework that maximizes system throughput by coordinating partitioned sub-tasks across satellites and ground stations. To realize this hierarchical design, HiO2 incorporates two key mechanisms: (i) a swarm-level offloading strategy that assigns tasks to swarms according to their optimal allocation, and (ii) a node-level runtime-aware partitioning and sub-task offloading scheme that dynamically determines both the number of partitions and the placements of cut-points, while coordinating the execution of sub-tasks across nodes based on workload and network conditions. Extensive experiments on a prototype system demonstrate that HiO2 not only outperforms state-of-the-art methods in system throughput and average task response time but also consistently meets real-time task deadlines.

This paper introduces a novel system architecture that integrates an active simultaneous transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS) with rate-splitting multiple access (RSMA) to achieve wireless covert communication in the presence of a multi-antenna warden. Departing from conventional approaches that deploy STAR-RIS at fixed positions, we treat the aSTAR-RIS location as a design variable to be optimized for maximizing the covert transmission rate. Our objective is to jointly optimize the aSTAR-RIS placement, transmit beamforming, transmission/reflection coefficients (TRCs), and common rate allocation so as to maximize both the common and private rates for the covert user. The resulting multi-variable optimization problem is addressed via an alternating optimization (AO) framework, which decomposes the problem into four tractable subproblems. For the multi-ratio fractional programming subproblem in the context of transmit beamforming and TRCs optimization, we adopt a Lagrangian dual formulation and quadratic transformation to reformulate the objective into a difference of convex (DC) form. The rank-one constrained beamforming design is then resolved using a penalized successive convex approximation (SCA) method combined with Gaussian randomization. A closed-form solution is derived for the common rate allocation subproblem, while the aSTAR-RIS location optimization is efficiently tackled via the SCA technique. Simulation results validate that the proposed scheme substantially enhances covert transmission rates while preserving communication covertness and guaranteeing reliable common signal reception. Importantly, the results highlight that optimizing the aSTAR-RIS location plays a critical role in improving the overall covert performance of the system.

As a promising paradigm, Satellite Edge Computing (SEC) enables new opportunities for facilitating intelligent processing onboard, crucial for the timely execution of mission-critical tasks. These tasks typically involve high data capture rates and rely on compute-intensive Deep Neural Network (DNN) models. However, a single satellite struggles to handle these tasks promptly due to its limited computational capabilities. Thus, effective collaboration within the SEC network is urgently needed to adapt to diverse capture rates, optimize resource utilization, and ensure real-time responses. Motivated by the fact that partitioning a DNN model can accelerate task inference and make better use of idle resources by simultaneous sub-task execution and reduced transmitted data, we propose HiO2, a hierarchical task offloading framework that maximizes system throughput by effective collaboration among satellites and ground stations to process the partitioned sub-tasks. This highlights the challenge of designing effective task partitioning and offloading strategies in dynamic, resource-constrained networks. HiO2 addresses this challenge with two key methods. First, it adopts a distributed swarm-level task offloading strategy that assigns tasks to swarms based on their optimal quantity. Second, HiO2 introduces a distributed node-level partitioning and offloading scheme, which dynamically identifies efficient cut-points according to workload and network dynamics, then offloads sub-tasks by collaboration among nodes in each swarm. Extensive data-driven evaluations demonstrate that, compared to the state-of-the-art baselines, HiO2 improves throughput to 1.19×, reduces average task completion time to 69.7%, and consistently meets task deadlines.

This paper proposes the Dual-Response Collision Tree Anti-Collision Protocol (DRCT), redesigning tree-based approaches to address performance degradation in large-scale RFID identification. DRCT achieves optimization through a systemic design based on three key integrated mechanisms: Firstly, a novel Tag Grouping mechanism, fundamentally distinct from collision-bit-dependent methods, uses magnitude comparison against a reader-defined dynamic prefix (preStr). This eliminates the need to analyze the immediate collision for partitioning, enabling immediate parallel R0/R1-cycle processing readiness. Secondly, to effectively manage the identification process under this novel partitioning scheme, DRCT introduces a crucial Localized Search Control mechanism. This relies on tag-side counters (seDepth) which are conditionally updated by incrementing only when a tag is involved in an R1-cycle (

Tenuous subgraph finding aims to detect a subgraph with few social interactions and weak relationships among nodes. Despite significant efforts made on this task, they are mostly carried out in view of graph-structured data. These methods depend on calculating the shortest path and need to enumerate all the paths between nodes, which suffer the combinatorial explosion. Moreover, they all lack the integration of neighborhood information. To this end, we propose a novel model named Graph Neural Network with Motif-aware for tenuous subgraph finding (GNNM), a neighborhood aggregation-based GNN framework that can capture the latent relationship between nodes. We design a GNN module to project nodes into a low-dimensional vector combining the higher-order correlation within nodes based on a motif-aware module. Then we design greedy algorithms in vector space to obtain a tenuous subgraph whose size is greater than a specified constraint. Particularly, considering that existing evaluation indicators cannot capture the latent friendship between nodes, we introduce a novel Potential Friend concept to measure the tenuity of a graph from a new perspective. Experimental results on the real-world and synthetic datasets demonstrate that our proposed method GNNM outperforms existing algorithms in efficiency and subgraph quality.

Abstract Finding groups in networks is very common in many practical applications, and most work mainly focus on dense groups. However, in scenarios like reviewer selection or weak social friends recommendation, we need to emphasize the privacy of individuals or minimize the possibility of information dissemination. So the internal relationship between individuals should be as tenuous as possible, but existing works cannot suit well to the requirement. Some works have focused on finding tenuous groups. However, these works only aim to find the most tenuous group and do not consider containing certain vertices. In this paper, we study the problem of finding tenuous groups in attributed networks that contain specific vertices. We first propose a new problem called Tenuous Attributed Group Query, and a new indicator, k-tenuity, to measure the structural tenuity of a group. Then we propose a method TAG-Basic to find proper groups by gradually selecting the vertices with optimal influence. We further design an advanced method TAG-ADV to improve the efficiency by forming a candidate set before selecting the optimal vertex. Experiment results show that k-tenuity is more effective than other state-of-the-art measurements, and our methods obtain the best result on group quality compared with other benchmark methods.

Traffic speed inference enables many applications that are essential for everyday life. Most traffic-prediction approaches assume that a constant number of sensors are deployed on the roads, whether they are either stationary loop detectors or vehicles equipped with Global Positioning System (GPS) tracking devices. The static nature of those fixtures limits their ability to adapt to scenarios that are more dynamic. Rather than relying on several fixed sensors to detect changes and infer traffic, we use crowdsourcing to judiciously select individuals and then make predictions. Our solution consists of three core components: dynamic seed selection, regional cluster building and ensemble traffic prediction. In the first phase, we employ Efficient Transition Probability (ETP) to evaluate candidate seed sets. Road clusters are then formed using hierarchical clustering that is tweaked by a dynamic programming technique. This method assesses the eccentricity of every cluster to bond every road within each cluster more closely. Subsequently, we develop an ensemble-learning strategy in conjunction with Lasso regression to forecast traffic. The strength of our ensemble approach is its ability to manage absent seeds, a condition that has never been investigated, to our knowledge. Substantial experimental evaluation indicates that our claim of dynamic updates is valid and effective. Our solution outperforms the state-of-the-art techniques by a wide margin, in terms of prediction accuracy.

Abstract Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.

Abstract In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics‐based Overlapping Semantic Community detection algorithm(DOSC) for node‐attribute networks. The method is divided into three phases: Firstly, we detect local single‐attribute subcommunities in each attribute‐induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real‐world networks demonstrate that DOSC is a more effective semantic community detection method compared with state‐of‐the‐art methods.

Abstract In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description. In this article, we only focus on the node‐attributed graph. According to the properties of structure and attribute in node‐attributed graphs, we define a novel dense subgraph pattern, called hybridized k‐truss in attribute‐augmented graph. A hybridized k‐truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least (k − 2) common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node‐attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over state‐of‐the‐art methods, through comparison about structural cohesiveness and attributive homogeneity.

Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters. For attributed networks, apart from the denseness requirement of topology structure, the attributes of nodes in the same community should also be homogeneous. Network embedding has been proved extremely useful in a variety of tasks, such as node classification, link prediction, and graph visualization, but few works dedicated to unsupervised embedding of node features specified for clustering task, which is vital for community detection and graph clustering. By post-processing with clustering algorithms like k -means, most existing network embedding methods can be applied to clustering tasks. However, the learned embeddings are not designed for clustering task, they only learn topological and attributed information of networks, and no clustering-oriented information is explored. In this article, we propose an algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs. Specifically, the presented work introduces a framework that simultaneously learns graph structure-based representations and clustering-oriented representations together. The framework consists of the following three modules: graph convolutional autoencoder module, soft modularity maximization module, and self-clustering module. Graph convolutional autoencoder module learns node embeddings based on topological structure and node attributes. We introduce soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks. By integrating clustering loss and embedding loss, NEC can jointly optimize node cluster labels assignment and learn representations that keep local structure of network. This model can be effectively optimized using stochastic gradient algorithm. Empirical experiments on real-world networks and synthetic networks validate the feasibility and effectiveness of our algorithm on community detection task compared with network embedding based methods and traditional community detection methods.

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy.

Abstract In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods.

Abstract Identifying the change‐prone parts of software could help managers and developers to effectively allocate maintenance resource and time during early phases of software life cycle. Change‐proneness prediction on file level with binary classification methods makes such identification possible. As the fact that change‐prone files frequently account for a small part of all the files, the prediction performance of standard classification methods is not satisfying. In this paper, we employ imbalanced learning methods, including bagging, resampling, and especially their combination to reduce the performance decrease of standard classifiers caused by the class imbalance problem in change‐proneness prediction. Besides, we propose a boxplot‐based partition method to provide more proper change‐proneness label designation for the training data. Eight open‐source Java projects are chosen in the empirical study to validate the effectiveness of the combination methods in change‐proneness prediction. The experimental results of the empirical study show that combining bagging with resampling can significantly improve the prediction performance of only bagging or resampling. Of all the combination methods employed, combination of bagging with undersampling performs better than others. And support vector machine is more effective as a base classifier than J48 and naive Bayes.

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection, in this article, we propose an algorithm, called link‐based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties. Experimental results on a large number of real‐world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks.

Not only the expertise of people but also the collaboration among people are of great importance for a team. Given a set of experts with different skills, a social network that reflects the collaboration among people and a task, the team formation problem in social networks aims at forming a team to complete the task. The team is required to satisfy the skill requirements of the task and collaborates efficiently. Different communication cost functions have been proposed to have a good measure on the collaboration strength of a team in the existing work. However, the grouped organization structure inside team is never considered, which is very common in real life scenarios. In a grouped team, we are more concerned with the collaboration among people in same group and among leaders. In this paper, a novel communication cost function for a grouped team is proposed, and we define the Grouped Team Formation problem. To solve the problem, an exact algorithm is proposed. We further modify the exact algorithm to propose two heuristic algorithms with higher efficiency. Extensive experiments evaluate the effectiveness and efficiency of the proposed methods, and validate the reasonability of our problem definition in practical settings.

The boom of social networking services makes it convenient to organize or participate in social events. Recent studies consider the social event organization problem, but they cannot provide diverse choices for users when there are multiple events. In this paper, we seek to devise an event organization scheme that provides diverse choices for users. For this goal, we explicitly distinguish the subjective preferences of users to events and the objective preferences of users to events. The former is considered to be the generative probabilities of users appearing in events, and the latter is viewed as the posterior probabilities of users participating in events. The Expectation–Maximization algorithm is employed to connect them together. After extracting these features, we extract an edge-weighted bipartite graph as the scheme from all the objective preferences. It captures the maximum sum of the objective preferences, and simultaneously satisfies soft user capacity constraints and hard event capacity constraints. We prove this problem is in P via transforming it into linear program (LP). In consideration that LP may have no feasible solutions, we alternatively devise an efficient polynomial time algorithm which can yield an approximately feasible solution. Experimental evaluation shows the effectiveness and efficiency of our proposed method.

Concept drift in data stream poses many challenges and difficulties in mining this tradition-distinct database. In this paper, we focus on detecting concept drift in evolving data stream. We propose a novel method to detect concept drift using entropy over an adaptive sliding window. In the method, the sliding window is not fixed but dynamically determined. Another distinct property is that the method integrates an algorithm to find the exact timestamp for retraining the classifier whenever a concept drift is detected. In the experiments, we evaluate our method on publicly available data streams containing various types of concept drifts, and then compare it with four well known concept drift detection methods. The experimental results show that compared with four benchmarks, the proposed method is better than or comparable with other methods for most cases.

A stable and efficient anti-collision protocol is very important for tags identification in many radio frequency identification (RFID) system such as flow productions and automatic controls. In this paper, we introduce an efficient anti-collision protocol named collision tree protocol (CT) and analyze the stability of it in detail. Both theoretical results and experimental results indicate that the performance of CT is only dependent on the number of tags to be identified and not influenced by the distribution of tag IDs and other factors, and the average performance of CT for one-tag identification converges to a constant. According to the definition of the stability of an anti-collision protocol, CT is a stable anti-collision protocol for RFID tag identification. The stability proposed in this paper provides a new performance metric for evaluating the performance of different anti-collision protocols for RFID tag identification. As a stable protocol, the time delay and the power consumption for one-tag identification of CT tend to be constant. Therefore, CT can be applied to various tags identification systems and especially suited to the RFID systems in which both the time delay and the power consumption for tags (or objects) identification are required to be limited and stable.

Constrained Delaunay triangular irregular network (CD-TIN) is a basic data structure widely used in GIS, 3D reconstruction, computer geometry and geosciences. Traditional researches on Delaunay triangular irregular networks (D-TIN) paid more attention to the insertion algorithms for points and edges, while little to the deletion algorithms for points and edges. The present algorithms for D-TIN are far insufficient for the dynamic updating of CD-TIN, which demands for not only the insertion for points and edges, but also the deletion for points and constrained edges. Based on the improvements to the present insertion and deletion algorithms for D-TIN, an algorithm for point deletion in CD-TIN, namely integral ear elimination (IEE), which improved from the EE algorithm for D-TIN, is presented. Some examples are demonstrated that the presented algorithms in this paper for the updating of CD-TIN are efficient.