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Graph neural networks for network science: A mini review

EPL 2025年第2期150卷 21001-21001页

作者： Li, Jia Ji, Yixiu Zhang, Yu Ding, Jie Li, Cong Li, Xiang Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Engineering Fudan University Shanghai 200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai 200433 China

Graph Neural networks (GNNs), a class of neural networks specifically designed to handle graph-structured data, have emerged as a hot research topic in fields such as data mining, machine learning, and biomedical engineering. Network science studies the properties and behaviors of complex networks, with graphs serving as the mathematical abstraction of these systems. In recent years, GNN technology has demonstrated significant application potential in network science. In this letter, we provide a mini review of different types of GNNs, as well as their applications in important network science tasks, particularly in nodes and edges identification as well as network robustness. Finally, we discuss the promising future research directions. Copyright © 2025 EPLA. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

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Toward the minimum vertex cover of complex networks using distributed potential games

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Science China(Information Sciences) 2023年第1期66卷 244-263页

作者： Jie CHEN Xiang LI Adaptive Networks and Control Lab Department of Electronic Engineering Fudan University Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji University

Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.

关键词： minimum vertex cover Nash equilibrium convergence rate scale-free network random graph

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An Evolutionary Game Optimization to Vertex Cover of Dynamic networks 33

An Evolutionary Game Optimization to Vertex Cover of Dynamic...

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第三十三届中国控制会议

作者： LI Ang TANG Chang Bing LI Xiang Adaptive Networks and Control Lab Electronic Engineering Department Fudan University

ISBN: (纸本)9781479947249

Vertex cover is one of the best known combinatorial optimization problems. Treating each vertex as an intelligent rational agent, we model vertex cover problem under the framework of evolutionary game theory, where players have different rates of forming and breaking links, and such linking dynamics introduces a transformation of the payoff matrix. We propose an algorithm to solve the vertex cover problem of dynamic networks, and find that a better approximate solution to the minimum vertex cover of dynamic networks can be obtained by choosing proper parameters.

关键词： Vertex cover,dynamic networks,evolutionary game theory,Nash equilibrium.

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Self-triggered consensus of multi-agent systems via model predictive control

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IFAC-PapersOnLine 2016年第4期49卷 19-24页

作者： Zhan, Jingyuan Li, Xiang Adaptive Networks and Control Lab Research Center of Smart Networks and Systems School of Information Science and Technology Fudan University Shanghai200433 China

This paper proposes a self-triggered consensus algorithm for multi-agent systems by using model predictive control (MPC), where the self-triggering rule and the control algorithm are optimized jointly. The proposed self-triggered MPC consensus algorithm drives the system to reach consensus asymptotically under mild assumptions, if the communication topology is connected. Numerical examples are finally presented to verify the effectiveness and advantages of the self-triggered MPC consensus algorithm. By comparing with the conventional time-triggered and event-triggered consensus algorithms, the self-triggered MPC consensus algorithm is shown to achieve equivalent performance with significant reduction of the numbers of controller updates and information transmission. © 2016, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multi agent systems Model predictive control Predictive control systems Communication topologies consensus Consensus algorithms Event triggered Event triggered controls Information transmission Self triggered controls Self triggering

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On the Priority Maximum Matching of Structural controllability of Temporal networks

On the Priority Maximum Matching of Structural Controllabili...

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第三十二届中国控制会议

作者： PAN Yujian LI Xiang ZHAN Jingyuan Adaptive Networks and Control Lab Electronic Engineering DepartmentFudan University

ISBN: (纸本)9781479900305

Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. The empirical analyses of temporal networks indicate that insufficient attentions have been paid to the embedded temporal features,and to the most interest of this paper,the study on structural controllability of temporal networks is *** propose the Priority Maximum Matching method in this paper to explore structural controllability of temporal networks,with which we locate as-least-as-possible driven nodes required by the structural *** also verify the feasibility and reliability of the proposed method with several data sets of empirical temporal networks,which shade new light on the study of controllability without wiping out the temporal information.

关键词： Temporal networks Structural controllability Priority Maximum Matching

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Consensus of Multiple Double-integrators With Aperiodic Sampled-data and Switching Topology 34

Consensus of Multiple Double-integrators With Aperiodic Samp...

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第三十四届中国控制会议

作者： ZHAN Jingyuan LI Xiang Adaptive Networks and Control Lab and Research Center of Smart Networks and SystemsSchool of Information Science and Technology Fudan University

ISBN: (纸本)9781467374439

This paper is concerned with the sampled-data consensus problem in networks of multiple double-integrator agents. It is assumed that the sampling intervals are nonuniform and the communication topology is dynamically changing. By employing the tools of nonnegative matrix theory and graph theory, it is proved that consensus can be reached if the communication digraph contains a directed spanning tree jointly and the sampling intervals are restricted to a feasible region with respect to the control gain and the max in-degree of the associated communication digraph. A numerical example is provided to illustrate the theoretical result.

关键词： consensus,double-integrator,aperiodic sampled-data,multi-agent systems,switching topology

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Cooperation and distributed optimization for the unreliable wireless game with indirect reciprocity

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Science China(Information Sciences) 2017年第11期60卷 129-145页

作者： Changbing TANG Xiang LI Zhen WANG Jianmin HAN College of Mathematics Physics and Information Engineering Zhejiang Normal University Adaptive Networks and Control Lab Department of Electronic EngineeringFudan University Research Center of Smart Networks and Systems Fudan University School of Cyberspace Hangzhou Dianzi University

Cooperation in packet forwarding among users and operators of a distributed wireless network has been widely studied. However, because of the limited computational resources, users in wireless communication do not prefer to cooperate with others unless cooperation may improve their own performance. Therefore, the key problem in cooperation enforcement must be solved first to enable a wireless network to be efficient. Yet,most of the existing game-theoretic cooperation stimulation approaches assume that the interactions between any pair of players(users) are long-lasting. In this paper, we apply game theory to optimize the communication efficiency of a distributed wireless network with finite number of interactions between any pair of players. Based on the mechanism of indirect reciprocity, we theoretically analyze the optimal action rule with the method of dynamic programming, and derive the approximate threshold of benefit-to-cost ratio to achieve the optimal action rule. Furthermore, we adopt the replicator dynamics to assess the evolutionary stability of the optimal action rule against the perturbation effect. Numerical illustrations verify the performance of the proposed method on wireless cooperation.

关键词： wireless cooperation distributed optimization game theory indirect reciprocity optimal action evolutionary stability

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The impact of information dissemination on vaccination in multiplex networks

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Science China(Information Sciences) 2022年第7期65卷 185-196页

作者： Xiao-Jie LI Cong LI Xiang LI Adaptive Networks and Control Lab Department of Electronic EngineeringFudan University Research Center of Smart Networks and Systems School of Information Science and EngineeringFudan University MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University

The impact of information dissemination on epidemic control is essentially subject to individual behaviors. Vaccination is one of the most effective strategies against the epidemic spread, whose correlation with the information dissemination should be better understood. To this end, we propose an evolutionary vaccination game model in multiplex networks by integrating an information-epidemic spreading process into the vaccination dynamics, and explore how information dissemination influences vaccination. The spreading process is described by a two-layer coupled susceptible-alert-infected-susceptible(SAIS) model, where the strength coefficient between two layers characterizes the tendency and intensity of information *** find that the impact of information dissemination on vaccination decision-making depends on not only the vaccination cost and network topology, but also the stage of the system evolution. For instance, in a two-layer BA scale-free network, information dissemination helps to improve vaccination density only at the early stage of the system evolution, as well as when the vaccination cost is smaller. A counter-intuitive conclusion that more information transmission cannot promote vaccination is obtained when the vaccination cost is larger. Moreover, we study the impact of the strength coefficient and individual sensitivity on the fraction of infected individuals and social cost, and unveil the role of information dissemination in controlling the epidemic.

关键词： evolutionary game theory epidemic modeling network dynamics

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TSGN: Transaction Subgraph networks Assisting Phishing Detection in Ethereum

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Jinhuan Chen, Pengtao Xu, Xinyao Wu, Jiajing Shen, Meng Xuan, Qi Yang, Xiaoniu Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou310023 China ZJUT Binjiang Institute of Artificial Intelligence Hangzhou310056 China Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering School of Information Science and Technology Shanghai200433 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Shenzhen518066 China National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China

Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct the Transaction SubGraph Network (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%∼6.71% relative improvement over previous state-of-the-art. © 2004-2012 IEEE.

关键词： Phishing

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Recent Advances and Challenges on control of Complex networks 35

Recent Advances and Challenges on Control of Complex Network...

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第35届中国控制会议

作者： HOU Baoyu LI Xiang LI Cong Adaptive Networks and Control Lab Department of Electronic Engineering Fudan University Research Center of Smart Networks & Systems School of Information Science & Engineering Fudan University

ISBN: (纸本)9781509009107

Network exists because of human, and plays a vital role also because of our actual requirements. Many networks in the real-world cannot keep working properly without control theory or control behavior. Essentially, control is a combinatorial optimization problem. On the one hand, we aim to achieve the control objectives better and faster;on the other hand, we hope to reduce resource consumption as much as possible. From the perspective of the network science, to re-examine the control of network, network controllability has maintained a focus in this field. In this article, we mainly focus on network structural controllability, state controllability and other related issues, review some valuable research findings, and highlight the prospect of relevant issues which deserves concern.

关键词： Structural controllability State controllability Temporal Network

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