一种面向符号网络社区检测攻击的新算法  

A Novel Algorithm for Community Detection Attacks in Signed Networks

作　　者：徐文娟 杨智翔 许小可 XU Wenjuan;YANG Zhixiang;XU Xiaoke(School of Information and Communication Engineering,Dalian Minzu University,Dalian 116600,China;School of Journalism and Communication,Beijing Normal University,Beijing 100875,China)

机构地区：[1]大连民族大学信息与通信工程学院,大连116600 [2]北京师范大学新闻传播学院,北京100875

出　　处：《指挥与控制学报》2025年第1期69-78,共10页Journal of Command and Control

基　　金：国家自然科学基金(62173065);北京市自然科学基金(4242040);北京市社会科学基金(21DTR040)资助。

摘　　要：传统社区检测攻击多聚焦无符号领域,应用于符号网络上存在忽略符号属性、算法效率和质量不高的问题。提出了一种符号网络社区检测攻击算法。设计了一种考虑符号属性和攻击操作的个体编码方式;进化时不使用检测算法计算适应度值,简化攻击流程以提升算法效率;设计了一种基于攻击符号模块度的局部策略,提升算法的攻击性能。将所提优化策略迁移至不同攻击框架上,在模型和实证网络上验证了攻击算法的通用性、鲁棒性和可移植性。Traditional methods for community detection attacks primarily focus on unsigned networks,leading to challenges such as the neglect of signed attributes,low algorithmic efficiency,and suboptimal quality when applied to signed networks.This study proposes a novel algorithm for community detection attack in signed networks.The algorithm introduces an individual encoding method that incorporates signed attributes and attack operations.During the evolutionary process,the fitness value is computed without relying on detection algorithms,thereby simplifying the attack process and improving algorithmic efficiency.Additionally,a local strategy based on the modularity of attacking signs is designed to enhance the algorithm's attack performance.The proposed optimization strategy was adapted to various attack frameworks,and the universality,robustness,and transferability of the attack algorithm were validated on both model networks and empirical real-world networks.

关 键 词：社区检测攻击 符号网络 局部策略 模块度优化 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]