Pub Date : 2025-12-30DOI: 10.1109/TNSE.2025.3647918
Dini Wang;Peng Yi;Gang Yan;Feng Fu
Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties – reinforcing tie strengths between peripheral nodes while weakening those between hubs – robustly promote cooperation in both pairwise and group dilemmas. Importantly, this configuration enables heterogeneous networks to outperform homogeneous ones in fixation of cooperation, thereby adding to the conventional view that degree heterogeneity inhibits cooperative behavior under the local stochastic strategy update. We further test the generality of degree-inverse weighted social ties in promoting cooperation on $30, 000$ random networks and 13 empirical networks drawn from real-world systems. Finally, we unveil the underlying mechanism by examining the formation and evolution of cooperative ties under social ties with degree-inverse weights. Our systematic analyses provide new insights into how the network adjustment of tie strengths can effectively steer structured populations toward cooperative outcomes in biological and social systems.
{"title":"Evolutionary Dynamics of Pairwise and Group Cooperation in Heterogeneous Social Networks","authors":"Dini Wang;Peng Yi;Gang Yan;Feng Fu","doi":"10.1109/TNSE.2025.3647918","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3647918","url":null,"abstract":"Understanding how cooperation evolves in structured populations remains a fundamental question across diverse disciplines. The problem of cooperation typically involves pairwise or group interactions among individuals. While prior studies have extensively investigated the role of networks in shaping cooperative dynamics, the influence of tie or connection strengths between individuals has not been fully understood. Here, we introduce a quenched mean-field based framework for analyzing both pairwise and group dilemmas on any weighted network, providing interpretable conditions required for favoring cooperation. Our theoretical advances further motivate us to find that the degree-inverse weighted social ties – reinforcing tie strengths between peripheral nodes while weakening those between hubs – robustly promote cooperation in both pairwise and group dilemmas. Importantly, this configuration enables heterogeneous networks to outperform homogeneous ones in fixation of cooperation, thereby adding to the conventional view that degree heterogeneity inhibits cooperative behavior under the local stochastic strategy update. We further test the generality of degree-inverse weighted social ties in promoting cooperation on <inline-formula><tex-math>$30, 000$</tex-math></inline-formula> random networks and 13 empirical networks drawn from real-world systems. Finally, we unveil the underlying mechanism by examining the formation and evolution of cooperative ties under social ties with degree-inverse weights. Our systematic analyses provide new insights into how the network adjustment of tie strengths can effectively steer structured populations toward cooperative outcomes in biological and social systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5074-5091"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1109/TNSE.2025.3649386
Wagner A. Junior;Fábio Ramos;Alex B. Vieira;José Augusto M. Nacif;Ricardo S. Ferreira
Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable information that can be encoded in edge features and relations. In this survey, we present a comprehensive review and a novel taxonomy of Edge-Aware Graph Learning Methods, i.e., models that explicitly leverage edge information in the learning process. We trace the evolution of these methods from classical approaches through random walks to modern GNN architectures, including the emerging paradigm of Edge-Aware Graph Transformers. Through a comparative analysis, we demonstrate the consistent performance gains of these models over traditional node-centric approaches across a wide range of real-world applications and benchmarks. However, many challenges arise in this field. As such, we provide an explicit discussion of key limitations, particularly the scalability issues and computational overhead associated with many current architectures. Finally, by synthesizing the state-of-the-art and identifying open problems, this survey provides a clear roadmap to guide future research toward developing more efficient, scalable, and robust edge-aware models.
{"title":"A Survey on Edge-Aware Graph Learning Methods","authors":"Wagner A. Junior;Fábio Ramos;Alex B. Vieira;José Augusto M. Nacif;Ricardo S. Ferreira","doi":"10.1109/TNSE.2025.3649386","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649386","url":null,"abstract":"Graph Neural Networks (GNNs) have gained popularity as an efficient choice for learning on graph-structured data. However, most methods are node or graph-centered, often overlooking valuable information that can be encoded in edge features and relations. In this survey, we present a comprehensive review and a novel taxonomy of Edge-Aware Graph Learning Methods, i.e., models that explicitly leverage edge information in the learning process. We trace the evolution of these methods from classical approaches through random walks to modern GNN architectures, including the emerging paradigm of Edge-Aware Graph Transformers. Through a comparative analysis, we demonstrate the consistent performance gains of these models over traditional node-centric approaches across a wide range of real-world applications and benchmarks. However, many challenges arise in this field. As such, we provide an explicit discussion of key limitations, particularly the scalability issues and computational overhead associated with many current architectures. Finally, by synthesizing the state-of-the-art and identifying open problems, this survey provides a clear roadmap to guide future research toward developing more efficient, scalable, and robust edge-aware models.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5680-5697"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11319233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cooperative spectrum sensing (CSS) is a technique that exploits spatial diversity to enhance spectrum detection in cognitive radio (CR) networks. It involves multiple cognitive users to collaboratively sense spectrum bands and report their raw sensed data or local detection results to a fusion center to make spectrum allocation decisions for CR. The performance of CSS is often degraded by the non-stationarity of the wireless channels and limited computation and communication resources in the CR network. To address these challenges, we propose a novel joint optimization framework tailored for multidimensional resource-constrained CR networks in this paper, by simultaneously determining the user selection, the sensing-transmission-computation duration, and the allocation of communication and computation resources involved in the system, with the aim of maximizing the spectrum sensing performance under strict resource constraints. The proposed framework first derives a closed-form solution for optimal sensing-transmission-computation duration and then develops an efficient iterative algorithm for joint user selection and resource allocation. Simulation results show that the proposed framework significantly outperforms existing solutions without jointly considering the sensing, transmission, and computation processes and/or multidimensional resource limitations.
{"title":"Joint Optimization of Sensing, Communication, and Computation for Cooperative Spectrum Sensing","authors":"Xuesong Liu;Junkang Ge;Xiaoqian Li;Yansong Liu;Haoyu Tang;Gang Feng","doi":"10.1109/TNSE.2025.3649240","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649240","url":null,"abstract":"Cooperative spectrum sensing (CSS) is a technique that exploits spatial diversity to enhance spectrum detection in cognitive radio (CR) networks. It involves multiple cognitive users to collaboratively sense spectrum bands and report their raw sensed data or local detection results to a fusion center to make spectrum allocation decisions for CR. The performance of CSS is often degraded by the non-stationarity of the wireless channels and limited computation and communication resources in the CR network. To address these challenges, we propose a novel joint optimization framework tailored for multidimensional resource-constrained CR networks in this paper, by simultaneously determining the user selection, the sensing-transmission-computation duration, and the allocation of communication and computation resources involved in the system, with the aim of maximizing the spectrum sensing performance under strict resource constraints. The proposed framework first derives a closed-form solution for optimal sensing-transmission-computation duration and then develops an efficient iterative algorithm for joint user selection and resource allocation. Simulation results show that the proposed framework significantly outperforms existing solutions without jointly considering the sensing, transmission, and computation processes and/or multidimensional resource limitations.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5453-5470"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1109/TNSE.2025.3649358
Shuaishuai Liu;Jin Wang;Geyong Min;Jianhua Hu
Although Computing Power Network (CPN) as the new network computing paradigm which can fully improve the utilization rate of decentralized computing power resources, the dynamic and heterogeneous characteristics of multivariate workloads present significant challenges to maintaining the Quality of Service (QoS) under dynamic resource scheduling. Therefore, workload prediction should be considered to ensure elastic demand services. However, the existing workload prediction methods mainly focus on (1) a single-granularity perspective, and (2) struggle to adapt to dynamic and heterogeneous multivariate workload environments, such as traditional LSTM-based or CNN-based methods that fail to capture cross-granularity dependencies under varying workload patterns. To consider above problems, we propose Multi-Granularity Workload Ensemble and Feature Inference for Multivariate Computing Power Prediction (MG-WEP), which address the problem from a multi-granularity perspective. First, we develop a mutual information feature selection method using a variational inference network to identify key features, facilitating a comprehensive exploration of the relationships among workload variables from an attribute perspective. Then, the clustering method is used to cluster similar workloads, effectively capturing the relationships among them. Furthermore, a combined ensemble prediction method is applied on all clustered workloads to improve prediction accuracy by leveraging the distinctive characteristics of each cluster from object perspective. Finally, we have fully compared the proposed algorithm with eleven comparison methods and four evaluation metrics on three real-world workload trace datasets. The results show that the proposed method has superior prediction performance.
{"title":"MG-WEP: Multi-Granularity Workload Ensemble and Variational Inference for Multivariate Computing Power Prediction","authors":"Shuaishuai Liu;Jin Wang;Geyong Min;Jianhua Hu","doi":"10.1109/TNSE.2025.3649358","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649358","url":null,"abstract":"Although <italic>Computing Power Network</i> (CPN) as the new network computing paradigm which can fully improve the utilization rate of decentralized computing power resources, the dynamic and heterogeneous characteristics of multivariate workloads present significant challenges to maintaining the <italic>Quality of Service</i> (QoS) under dynamic resource scheduling. Therefore, workload prediction should be considered to ensure elastic demand services. However, the existing workload prediction methods mainly focus on (1) a single-granularity perspective, and (2) struggle to adapt to dynamic and heterogeneous multivariate workload environments, such as traditional LSTM-based or CNN-based methods that fail to capture cross-granularity dependencies under varying workload patterns. To consider above problems, we propose <italic>Multi-Granularity Workload Ensemble and Feature Inference for Multivariate Computing Power Prediction</i> (MG-WEP), which address the problem from a multi-granularity perspective. First, we develop a mutual information feature selection method using a variational inference network to identify key features, facilitating a comprehensive exploration of the relationships among workload variables from an attribute perspective. Then, the clustering method is used to cluster similar workloads, effectively capturing the relationships among them. Furthermore, a combined ensemble prediction method is applied on all clustered workloads to improve prediction accuracy by leveraging the distinctive characteristics of each cluster from object perspective. Finally, we have fully compared the proposed algorithm with eleven comparison methods and four evaluation metrics on three real-world workload trace datasets. The results show that the proposed method has superior prediction performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5471-5488"},"PeriodicalIF":7.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1109/TNSE.2025.3649013
Reza Khalvandi;Brunilde Sansò
Large-scale distributed wireless networks provide infrastructure-free and cost-effective connectivity, supporting applications from disaster recovery to global digital inclusion. However, multi-hop communication introduces scalability challenges, as point-to-point (P2P) capacity decreases with the number of intermediate relays (hop count). The growth rate of the expected hop count with network expansion is primarily governed by the underlying interaction patterns among network users. Thus, this study focuses on the critical role of multi-hop communication and user interaction probability, which empirical evidence shows it decays as a power law with geographic distance. We present a comprehensive analysis of network scalability, from capacity estimation to empirical evaluation of real-world interaction patterns. The capacity estimation problem is decomposed using a novel analytical methodology, along with symmetric topology selection and geometric partitioning, to overcome the complexities inherent in previous models. The estimated P2P capacity bounds, derived from expected hop count, surpass previous benchmarks. Specifically, when the power-law exponent exceeds a critical threshold, the expected hop count remains stable and P2P capacity is sustained; otherwise, the hop count grows and capacity declines as the network scales. Accordingly, an analytical method is devised to relate real-world interaction patterns to the power-law exponent, quantified by the contact distribution. The analysis of multiple empirical datasets confirms that the exponent falls within a range that naturally supports scalability. Consequently, multi-hop communication does not fundamentally hinder the wide-scale deployment of distributed wireless networks. This capacity-based analysis provides a clear perspective on scalability under realistic interaction patterns and underscores the promising future of such networks, as well as their potential for widespread deployment.
{"title":"Revisiting Scalability of Distributed Wireless Networks: A Multi-Hop Communication Perspective","authors":"Reza Khalvandi;Brunilde Sansò","doi":"10.1109/TNSE.2025.3649013","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649013","url":null,"abstract":"Large-scale distributed wireless networks provide infrastructure-free and cost-effective connectivity, supporting applications from disaster recovery to global digital inclusion. However, multi-hop communication introduces scalability challenges, as point-to-point (P2P) capacity decreases with the number of intermediate relays (<italic>hop count</i>). The growth rate of the expected <italic>hop count</i> with network expansion is primarily governed by the underlying interaction patterns among network users. Thus, this study focuses on the critical role of multi-hop communication and user interaction probability, which empirical evidence shows it decays as a power law with geographic distance. We present a comprehensive analysis of network scalability, from capacity estimation to empirical evaluation of real-world interaction patterns. The capacity estimation problem is decomposed using a novel analytical methodology, along with symmetric topology selection and geometric partitioning, to overcome the complexities inherent in previous models. The estimated P2P capacity bounds, derived from <italic>expected</i> hop count, surpass previous benchmarks. Specifically, when the power-law exponent exceeds a critical threshold, the expected hop count remains stable and P2P capacity is sustained; otherwise, the hop count grows and capacity declines as the network scales. Accordingly, an analytical method is devised to relate real-world interaction patterns to the power-law exponent, quantified by the <italic>contact distribution</i>. The analysis of multiple empirical datasets confirms that the exponent falls within a range that naturally supports <italic>scalability</i>. Consequently, multi-hop communication does not fundamentally hinder the wide-scale deployment of distributed wireless networks. This capacity-based analysis provides a clear perspective on scalability under realistic interaction patterns and underscores the promising future of such networks, as well as their potential for widespread deployment.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5780-5798"},"PeriodicalIF":7.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/TNSE.2025.3648495
Xiaohe Wang;Xinli Shi;Guanghui Wen;Xinghuo Yu
In federated learning, improving communication efficiency is a critical challenge, especially under partial participation and biased compression. Many existing approaches rely on unbiased compression or strong assumptions, such as the bounded gradient assumption, which are often difficult to satisfy in practice. In this paper, we propose a novel federated learning algorithm named EF21-MP (EF21 with Momentum and Partial Participation), which combines biased compression with partial participation and stochastic gradient descent. Furthermore, it incorporates momentum and EF21 to reduce variance from stochastic gradient descent and biased compression. It achieves convergence for nonconvex optimization under standard smoothness and bounded variance conditions, without relying on any bounded gradient assumptions, and could support for batch-free training. The numerical results demonstrate that EF21-MP consistently outperforms the existing baselines.
在联邦学习中，提高通信效率是一个关键的挑战，特别是在部分参与和偏压缩的情况下。许多现有的方法依赖于无偏压缩或强假设，如有界梯度假设，这些假设在实践中往往难以满足。在本文中，我们提出了一种新的联合学习算法EF21- mp (EF21 with Momentum and Partial Participation)，它结合了偏压、偏参与和随机梯度下降。此外，该方法还结合了动量和EF21来减小随机梯度下降和偏压带来的方差。它在标准平滑和有界方差条件下实现了非凸优化的收敛性，不依赖于任何有界梯度假设，可以支持无批处理训练。数值结果表明，EF21-MP始终优于现有基线。
{"title":"EF21 With Momentum and Partial Participation for Non-Convex Federated Learning Under Biased Compression","authors":"Xiaohe Wang;Xinli Shi;Guanghui Wen;Xinghuo Yu","doi":"10.1109/TNSE.2025.3648495","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3648495","url":null,"abstract":"In federated learning, improving communication efficiency is a critical challenge, especially under partial participation and biased compression. Many existing approaches rely on unbiased compression or strong assumptions, such as the bounded gradient assumption, which are often difficult to satisfy in practice. In this paper, we propose a novel federated learning algorithm named EF21-MP (EF21 with Momentum and Partial Participation), which combines biased compression with partial participation and stochastic gradient descent. Furthermore, it incorporates momentum and EF21 to reduce variance from stochastic gradient descent and biased compression. It achieves convergence for nonconvex optimization under standard smoothness and bounded variance conditions, without relying on any bounded gradient assumptions, and could support for batch-free training. The numerical results demonstrate that EF21-MP consistently outperforms the existing baselines.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5539-5550"},"PeriodicalIF":7.9,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-26DOI: 10.1109/TNSE.2025.3648844
Changbing Bi;Yue Cao;Yanzhen Ren;Youliang Tian;Lin Wan;Wei Ke
With the rapid development of the Electric Vehicle (EV) market, the growing demand for electricity charging has driven the evolution of private charging infrastructure toward shared deployment. Shared Charging Systems (SCSs) play a vital role in integrating both private and public Charging Piles (CPs), thereby improving overall resource utilization. However, such multi-party SCSs introduce challenges in security and fairness. EVs and CPs exchange parameters via wireless communication to optimize the charging process, which may exposes various threats such as tampering, eavesdropping, replay, and deletion attacks. Meanwhile, the distributed deployment of CPs complicates charging fee calculation management and may lead to issues such as malicious overcharging. To address these challenges, we propose a blockchain-enabled secure authentication and fair trading scheme, ensuring secure communication while guaranteeing transparency and fairness for SCSs. First, we design a pairing-free heterogeneous signcryption algorithm that supports distributed key generation. It realizes efficient mutual authentication based on this algorithm while preventing information leakage during the process. We provide a rigorous security proof under the Random Oracle Model (ROM) to establish its security. Second, we develop a blockchain-based smart contract mechanism to enable decentralized and transparent charging fee calculation, as well as automated payments. By eliminating third-party intermediaries, our solution reduces trading costs whle effectively addresses challenges such as charging fee calculation difficulties and malicious overcharging in distributed CP deployments. Experimental results show that the proposed scheme outperforms existing approaches in terms of both computational and communication overhead. Additionally, our smart contracts incur extremely low gas costs, enhancing the feasibility of the scheme.
{"title":"A Blockchain-Enabled Secure Authentication and Fair Trading Scheme for Shared Charging Systems","authors":"Changbing Bi;Yue Cao;Yanzhen Ren;Youliang Tian;Lin Wan;Wei Ke","doi":"10.1109/TNSE.2025.3648844","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3648844","url":null,"abstract":"With the rapid development of the Electric Vehicle (EV) market, the growing demand for electricity charging has driven the evolution of private charging infrastructure toward shared deployment. Shared Charging Systems (SCSs) play a vital role in integrating both private and public Charging Piles (CPs), thereby improving overall resource utilization. However, such multi-party SCSs introduce challenges in security and fairness. EVs and CPs exchange parameters via wireless communication to optimize the charging process, which may exposes various threats such as tampering, eavesdropping, replay, and deletion attacks. Meanwhile, the distributed deployment of CPs complicates charging fee calculation management and may lead to issues such as malicious overcharging. To address these challenges, we propose a blockchain-enabled secure authentication and fair trading scheme, ensuring secure communication while guaranteeing transparency and fairness for SCSs. First, we design a pairing-free heterogeneous signcryption algorithm that supports distributed key generation. It realizes efficient mutual authentication based on this algorithm while preventing information leakage during the process. We provide a rigorous security proof under the Random Oracle Model (ROM) to establish its security. Second, we develop a blockchain-based smart contract mechanism to enable decentralized and transparent charging fee calculation, as well as automated payments. By eliminating third-party intermediaries, our solution reduces trading costs whle effectively addresses challenges such as charging fee calculation difficulties and malicious overcharging in distributed CP deployments. Experimental results show that the proposed scheme outperforms existing approaches in terms of both computational and communication overhead. Additionally, our smart contracts incur extremely low gas costs, enhancing the feasibility of the scheme.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5603-5621"},"PeriodicalIF":7.9,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1109/TNSE.2025.3647512
Rong Wang;Runyu Mao;Tao Wen;Shihong Wei;Qian Li;Yunpeng Xiao
In social networks, the accurate detection of guided topics is of great significance for maintaining the healthy order of the network. Aiming at the high-dimensionality of its feature space and the hiddenness of users' emotions, a guided topic detection method based on data enhancement and feature representation is proposed. Firstly, to address the problem of sparse effective data and high-dimensional heterogeneity in the early stage of guided topic, GAN network is introduced to realize homomorphic compensation of data and enhance data diversity. Meanwhile, the SC2vec method is designed to realize the low-rank densification of data. In addition, random wandering is introduced to mine the implicit association network among users and to realize the fusion of multi-dimensional information. Secondly, to address the problem of the hidden nature of users' emotional polarity, the internal attributes and external influences of users are mined. The fine-grained emotional influence factors based on linear multiple regression are constructed. At the same time, the evolutionary game theory is introduced to build an emotional interaction game model between users to reveal the dynamic evolution law of users' emotions. The experimental results show that the method not only successfully realizes the low-rank densification of data and the deep mining of implicit user emotions, but also achieves significant improvement in the accuracy of guided topic detection.
{"title":"A Guided Topic Detection Model Based on Data Augmentation and Feature Representation","authors":"Rong Wang;Runyu Mao;Tao Wen;Shihong Wei;Qian Li;Yunpeng Xiao","doi":"10.1109/TNSE.2025.3647512","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3647512","url":null,"abstract":"In social networks, the accurate detection of guided topics is of great significance for maintaining the healthy order of the network. Aiming at the high-dimensionality of its feature space and the hiddenness of users' emotions, a guided topic detection method based on data enhancement and feature representation is proposed. Firstly, to address the problem of sparse effective data and high-dimensional heterogeneity in the early stage of guided topic, GAN network is introduced to realize homomorphic compensation of data and enhance data diversity. Meanwhile, the SC2vec method is designed to realize the low-rank densification of data. In addition, random wandering is introduced to mine the implicit association network among users and to realize the fusion of multi-dimensional information. Secondly, to address the problem of the hidden nature of users' emotional polarity, the internal attributes and external influences of users are mined. The fine-grained emotional influence factors based on linear multiple regression are constructed. At the same time, the evolutionary game theory is introduced to build an emotional interaction game model between users to reveal the dynamic evolution law of users' emotions. The experimental results show that the method not only successfully realizes the low-rank densification of data and the deep mining of implicit user emotions, but also achieves significant improvement in the accuracy of guided topic detection.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5110-5127"},"PeriodicalIF":7.9,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1109/TNSE.2025.3645073
Qianqian Cai;Tong Wang;Mali Xing;Yanyan Ye;Minyue Fu
Community detection is capable of uncovering the inherent structure and functional organization within complex networks by analyzing multi-scale topological features. According to the brief survey, local detection methods demonstrate notable strengths, but challenges persist in selecting suitable seeds, identifying community cores, and precisely extending communities. Therefore, in this paper, a local community detection algorithm based on identifying high-quality core region is proposed. Specifically, at the seed selection stage, a core cohesiveness index is constructed to quantify the node importance, with two-step filtering strategy implemented to refine the selection of suitable seeds. After that, a hierarchical affinity evaluation mechanism is proposed on the basis of node-core affinity values to ensure the formation of high-quality core region (i.e., high-quality initial community). Community extension is then achieved by using the enhanced objective function combined with an incremental update strategy, it preserves structural cohesion and reduces computational costs. Finally, the membership assignments of the remaining nodes will be further processed through community optimization to refine community boundaries. Experimental results demonstrate that our proposed algorithm outperforms other community detection algorithms with relatively low time complexities across multi-scale real-world and synthetic networks.
{"title":"Local Community Detection in Complex Networks: A Brief Survey and an Algorithm Based on Identifying High-Quality Core Region","authors":"Qianqian Cai;Tong Wang;Mali Xing;Yanyan Ye;Minyue Fu","doi":"10.1109/TNSE.2025.3645073","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3645073","url":null,"abstract":"Community detection is capable of uncovering the inherent structure and functional organization within complex networks by analyzing multi-scale topological features. According to the brief survey, local detection methods demonstrate notable strengths, but challenges persist in selecting suitable seeds, identifying community cores, and precisely extending communities. Therefore, in this paper, a local community detection algorithm based on identifying high-quality core region is proposed. Specifically, at the seed selection stage, a core cohesiveness index is constructed to quantify the node importance, with two-step filtering strategy implemented to refine the selection of suitable seeds. After that, a hierarchical affinity evaluation mechanism is proposed on the basis of node-core affinity values to ensure the formation of high-quality core region (i.e., high-quality initial community). Community extension is then achieved by using the enhanced objective function combined with an incremental update strategy, it preserves structural cohesion and reduces computational costs. Finally, the membership assignments of the remaining nodes will be further processed through community optimization to refine community boundaries. Experimental results demonstrate that our proposed algorithm outperforms other community detection algorithms with relatively low time complexities across multi-scale real-world and synthetic networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5489-5504"},"PeriodicalIF":7.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Space-Air-Ground Integrated Networks (SAGIN) have been recognized as key enablers of 6G systems for ubiquitous service provisioning, unlocking Internet of Things (IoT) applications in geographically dispersed areas. In this paper, we study the problem of computation task offloading for remotely deployed IoT devices to either a limited-capability Uncrewed Aerial Vehicle (UAV)-mounted Multi-access Edge Computing (MEC) server or a cloud server via satellite relaying. The problem is formulated as a non-cooperative game, where each IoT device autonomously determines the percentage of task offloaded to each server to minimize the aggregate time and energy overhead due to transmissions and remote processing. Diverging from the prevailing literature, in this paper, we model the IoT devices' risk-seeking and loss-averse behavior in sharing the common pools of computing resources, i.e., cloud and MEC server. By incorporating risk-consciousness in their computation offloading decision-making, IoT devices strive to balance the total incurred overhead with the likelihood of task rejection due to overexploitation of limited shared edge resources. To this end, the IoT devices' utility function is modeled using Prospect Theory and Tragedy of the Commons. Two solutions based on normal and satisfaction-form games are derived, targeting to maximize or achieve a minimum value for the prospect-theoretic utility, providing insights from both device and system perspectives, respectively. Numerical results show the effectiveness of the overall risk-conscious computing framework in the achieved time and energy overhead, as well as task probability of failure.
{"title":"Risk-Conscious Computing in Space-Air-Ground IoT Networks: A Prospect-Theoretic Game Perspective","authors":"Panagiotis Charatsaris;Maria Diamanti;Eirini Eleni Tsiropoulou;Symeon Papavassiliou","doi":"10.1109/TNSE.2025.3647157","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3647157","url":null,"abstract":"Space-Air-Ground Integrated Networks (SAGIN) have been recognized as key enablers of 6G systems for ubiquitous service provisioning, unlocking Internet of Things (IoT) applications in geographically dispersed areas. In this paper, we study the problem of computation task offloading for remotely deployed IoT devices to either a limited-capability Uncrewed Aerial Vehicle (UAV)-mounted Multi-access Edge Computing (MEC) server or a cloud server via satellite relaying. The problem is formulated as a non-cooperative game, where each IoT device autonomously determines the percentage of task offloaded to each server to minimize the aggregate time and energy overhead due to transmissions and remote processing. Diverging from the prevailing literature, in this paper, we model the IoT devices' risk-seeking and loss-averse behavior in sharing the common pools of computing resources, i.e., cloud and MEC server. By incorporating risk-consciousness in their computation offloading decision-making, IoT devices strive to balance the total incurred overhead with the likelihood of task rejection due to overexploitation of limited shared edge resources. To this end, the IoT devices' utility function is modeled using Prospect Theory and Tragedy of the Commons. Two solutions based on normal and satisfaction-form games are derived, targeting to maximize or achieve a minimum value for the prospect-theoretic utility, providing insights from both device and system perspectives, respectively. Numerical results show the effectiveness of the overall risk-conscious computing framework in the achieved time and energy overhead, as well as task probability of failure.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5056-5073"},"PeriodicalIF":7.9,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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