网络与信息安全学报2024，Vol.10Issue(1)：79-90,12.DOI:10.11959/j.issn.2096-109x.2024013
基于多查询的社交网络关键节点挖掘算法
Multi-query based key node mining algorithm for social networks
摘要
Abstract
Mining key nodes in complex networks has been a hotly debated topic as it played an important role in solving real-world problems.However,the existing key node mining algorithms focused on finding key nodes from a global perspective.This approach became problematic for large-scale social networks due to the unacceptable storage and computing resource overhead and the inability to utilize known query node information.A key node mining algorithm based on multiple query nodes was proposed to address the issue of key suspect mining.In this method,the known suspects were treated as query nodes,and the local topology was extracted.By calculating the critical degree of non-query nodes in the local topology,nodes with higher critical degrees were selected for recom-mendation.Aiming to overcome the high computational complexity of key node mining and the difficulty of effec-tively utilizing known query node information in existing methods,a two-stage key node mining algorithm based on multi-query was proposed to integrate the local topology information and the global node aggregation feature in-formation of multiple query nodes.It reduced the calculation range from global to local and quantified the criti-cality of related nodes.Specifically,the local topology of multiple query nodes was obtained using the random walk algorithm with restart strategy.An unsupervised graph neural network model was constructed based on the graphsage model to obtain the embedding vector of nodes.The model combined the unique characteristics of nodes with the aggregation characteristics of neighbors to generate the embedding vector,providing input for similarity calculations in the algorithm framework.Finally,the criticality of nodes in the local topology was measured based on their similarity to the features of the query nodes.Experimental results demonstrated that the proposed algorithm outperformed traditional key node mining algorithms in terms of time efficiency and result effectiveness.关键词
社交网络/随机游走/图神经网络/节点嵌入向量/关键节点Key words
social network/random walk/graph neural network/node embedding vector/key node分类
信息技术与安全科学引用本文复制引用
辛国栋,朱滕威,黄俊恒,魏家扬,刘润萱,王巍..基于多查询的社交网络关键节点挖掘算法[J].网络与信息安全学报,2024,10(1):79-90,12.基金项目
国家自然科学基金(62272129) （62272129）
国家重点研发计划(2021YFB2012400) （2021YFB2012400）
中央高校基本科研业务费专项(HIT.NSRIF.2020098)The National Natural Science Foundation of China(62272129),The National Key R&D Program of Chi-na(2021YFB2012400),The Fundamental Research Funds for the Central Universities(HIT.NSRIF.2020098) （HIT.NSRIF.2020098）
