Node Importance Analysis Integrated with Community Assessment

doi:10.3778/j.issn.1002-8331.2205-0509

Abstract

Abstract: The research on mining key members in the target is an important branch in the field of social networks, but existing importance algorithms are prone to the phenomenon of aggregation of mined key nodes. To address this problem, a node importance algorithm integrated with community assessment is proposed, which defines a community importance assessment function based on the network topology of the target group, incorporating the internal influence of members in their community and external connectivity to comprehensively evaluate the importance of members. Taking four different complex networks as experimental data, compared with the existing algorithms, it is verified from three dimensions：propagation ability, robustness and Kendall correlation coefficient. The experiments show that the algorithm is more accurate in measuring the importance of members in the group.

Key words: social networking, key members, community, modularity

摘要： 针对在目标中挖掘关键成员的研究是社交网络领域的重要分支，但现有的重要性算法很容易出现挖掘的关键节点聚集现象。针对此问题，提出了一种融入社区评估的节点重要性算法，该算法根据目标群体网络拓扑结构，定义了社区重要性评估函数，融合了成员在其社区的内部影响力及外部连通性，综合评价成员重要度。以4个真实的复杂网络作为实验数据，与现有算法进行对比，从传播能力、鲁棒性和肯德尔相关系数三个维度验证，实验表明该算法对群体中的成员重要性度量更加准确。

关键词: 社交网络, 关键成员, 社区, 模块度

SUN Baibing, SUN Jiazheng, HE Quan, DU Yanhui. Node Importance Analysis Integrated with Community Assessment[J]. Computer Engineering and Applications, 2023, 59(3): 226-233.

孙百兵, 孙家政, 何泉, 杜彦辉. 融入社区评估的节点重要性分析[J]. 计算机工程与应用, 2023, 59(3): 226-233.

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