背景图增强的社交网络重要节点自适应排序算法  

Self-adaptive algorithm for ranking important nodes in socialnetworks enhanced by ground graph

作　　者：冯俊又 陈李舟 刘先博 徐煊翔 杜彦辉[1] Feng Junyou;Chen Lizhou;Liu Xianbo;Xu Xuanxiang;Du Yanhui(College of Information Network Security,People s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《计算机应用研究》2025年第3期742-748,共7页Application Research of Computers

基　　金：中国人民公安大学网络空间安全执法技术双一流专项资助项目(2023SYL07)。

摘　　要：社交网络中的重要节点对网络结构和功能具有决定性影响,开发精度更高的重要节点排序算法成为当前的研究热点之一。其中,LR(LeaderRank)引入一个背景节点明显提升了经典PageRank排序算法的性能,但仍面临着网络中小出度用户的投票权偏见问题。因此,提出背景图增强的社交网络重要节点自适应排序算法AGR(adaptive GraphRank),构建多节点背景图替代LR的单一背景节点,基于H指数设计有偏向的随机游走,缓解投票权偏见。调参实验初步确定了背景图的最优规模和结构;与K-TOPSIS等现有优秀算法进行对比实验,验证了AGR在传播、瓦解、鲁棒性三个关键维度上的性能提升;实际案例检验了算法在真实场景下的有效性。综上,AGR有效缓解了投票权偏见,提高了排序精度,展示出较优的性能和应用潜力。Important nodes in social networks have a decisive influence on the network structure and function.Developing more accurate ranking algorithms has become one of the current research hotspots.LR(LeaderRank)introduces a ground node,significantly improving the performance of the classic PageRank.However,it still faces the voting bias problem of users with small out-degrees in the network.Therefore,this paper proposed AGR(adaptive GraphRank),a self-adaptive algorithm for ranking important nodes in social networks enhanced by ground graph.AGR constructed a multi-node ground graph to replace LR’s single ground node and designed a biased random walk based on the H-index to alleviate voting bias.Preliminary para-meter experiments determine the optimal scale and structure of the ground graph.Compara tive experiments with excellent existing algorithms such as K-TOPSIS verify AGR’s performance improvements in three key dimensions:propagation,disintegration,and robustness.A real-world case study further demonstrates the effectiveness of the algorithm in practical scenarios.In conclusion,AGR effectively reduces voting bias,improves ranking accuracy,and shows superior performance and application potential.

关 键 词：重要节点 LeaderRank adaptive GraphRank 背景图 H指数 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]