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融合用户特征的信息交互网络关键节点动态识别研究

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Research on the Dynamic Identification of Key Nodes in Information Interaction Networks Integrated with User Characteristics

摘要: 摘 要:[目的/意义]关键节点对于网络舆情事件的发展态势具有重要影响,动态识别关键节点对网络舆情的管控与治理具有重要意义。[方法/过程] 本文模拟舆情网络中用户的信息交互过程,构建“信源层—交互枢纽层—信息受众层”三层级网络舆情信息交互模型。在此基础上,同时考虑五类用户特征及网络结构特征构建动态融合交互网络,利用Leiden算法和度中心性指标挖掘网络中的细粒度社群及其关键节点。[结果/结论]三层级信息交互模型为干预网络舆情发展提供了新的视角,从信息交互层级来看,前期交互枢纽层的关键节点起推动作用,后期信源层的关键节点起引导作用;从关键节点的数量及关联关系来看,舆情初期和末期的关键节点呈现“一超独存”格局,关键节点间联系不紧密,舆情爆发和扩散时期的关键节点呈现“多强并存”格局,且关键节点间的联系较为紧密。

Abstract: Abstract: [Purpose/Significance] Key nodes have a significant impact on the development trend of online public opinion events, and the dynamic identification of key nodes is of great importance for the control and governance of online public opinion.[Method/Process] This paper simulates the information interaction process among users in the public opinion network and constructs a three-tier network public opinion information interaction model of "information source layer - interaction hub layer - information audience layer". On this basis, a dynamic integrated interaction network is built by considering five types of user characteristics and network structure characteristics, and the Leiden algorithm and degree centrality index are used to mine fine-grained communities and key nodes within the network.[Results/Conclusion] The three-tier information interaction model provides a new perspective for intervening in the development of online public opinion. From the perspective of information interaction levels, key nodes in the interaction hub layer play a promoting role in the early stage, while key nodes in the information source layer play a guiding role in the later stage. From the perspective of the number and association of key nodes, key nodes in the early and late stages of public opinion exhibit a "single super dominant" pattern with loose connections between key nodes. In contrast, key nodes during the outbreak and diffusion stages of public opinion exhibit a "multiple strong coexistence" pattern with relatively close connections between key nodes.

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[V1] 2024-09-30 23:32:26 PSSXiv:202410.00159V1 下载全文
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