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A new centrality measure based on neighbor loop structure for network dismantling

Digital Communications and networks 2024年第2期10卷 472-480页

作者： Qingxia Liu Bang Wang Jiming Qi Xianjun Deng School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China

Nearly all real-world networks are complex networks and usually are in danger of ***,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network *** dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive *** overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation *** addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and ***,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation *** results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack ***,the insights gained from this study may be of assistance to future practical research into real-world networks.

关键词： Complex networks network dismantling Centrality measure

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GER-PSRO: Graph Embedding Representation based Reinforcement Learning in Sequential Colonel Blotto Game

GER-PSRO: Graph Embedding Representation based Reinforcement...

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第43届中国控制会议

作者： Junren Luo Wanpeng Zhang Xueqiang Gu Jiabing Wang Jing Chen National University of Defense Technology

In order to solve the problem of dismantling combat networks,a dismantling model of heterogeneous kill networks is first established,and then a sequential Colonel Blotto game model is *** optimal dismantling method for combat networks is proposed with graph embedding representation reinforcement learning aided policy space response oracle(GER-PSRO).The GER-PSRO method innovated a type coding-aggregation approach to embed the node type information and structure information in the heterogeneous kill network into the representation vector,and then trained the representation vector decoding through deep reinforcement learning to obtain the optimal dismantling ***,comparative experiments on heterogeneous kill networks are carried out,and the results show that the proposed GER-PSRO method can fully tap the node type information and structure information of heterogeneous networks,and can be used to approximate the Nash equilibrium of sequential Colonel Blotto games,quickly find the key nodes that affect the function of the network,and has good transferability for different combat networks.

关键词： Reinforcement Learning Policy Space Response Oracle Colonel Blotto Game network dismantling

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