基于层间影响的时序网络节点重要性识别
Importance Identification of Nodes in Sequential Network Based on Inter-Layer Influence
摘要: 现实生活中，复杂网络具有动态性和时变性，识别时序网络中的重要节点相比于静态网络更加具有挑战性。在本文中，我们提出了一种基于层间影响的超邻接矩阵在时序网络中的节点重要性识别方法。首先，我们定义了层间影响系数，其次考虑了时序网络层内和层间的关系构建了时序超邻接矩阵(TSAM)利用特征向量中心性对节点的中心性指标进行估。在多个实现数据集上的结果显示：相比经典的方法SAM以及SSAM，使用提出的算法得到的Kendall’s τ值在各时间层上均有显著提高，结果表明时序引力模型的度量对于时序网络的节点重要性度量具有十分重要的意义。
Abstract: In real life, complex networks are dynamic and time-varying, and it is more challenging to identify the important nodes in the temporal network than static networks. In this paper, we propose a method to identify the importance of nodes in temporal networks based on the super-adjacency matrix based on inter-layer influence. First, we define the inter-layer influence coefficient, and then we consider the relationship between the intra-layer and inter-layer of the temporal network to construct the temporal super-adjacency matrix (TSAM) to estimate the centrality index of the node using the centrality of the feature vector. The results on multiple implementation data sets show that compared with the classical methods SAM and SSAM, Kendall’s τ obtained by using the proposed algorithm. The results show that the measurement of temporal gravity model is very important for the measurement of node importance in temporal networks.
文章引用：单崴. 基于层间影响的时序网络节点重要性识别[J]. 运筹与模糊学, 2024, 14(1): 977-987. https://doi.org/10.12677/ORF.2024.141090

参考文献

[1] Peng, X.L., Xu, X.J., Fu, X., et al. (2013) Vaccination Intervention on Epidemic Dynamics in Networks. Physical Review E, 87, Article ID: 022813 [Google Scholar] [CrossRef] 
[2] 朱为华, 刘凯, 闫小勇, 汪明, 吴金闪. 识别流网络关键节点的虚拟外界投入产出分析法[J]. 电子科技大学学报, 2018, 47(2): 292-297.
[3] Ye, W., et al. (2021) Who Are the Celebrities? Identifying Vital Users on Sina Weibo Microblogging Network. Knowledge-Based Systems, 231, Article ID: 107438. [Google Scholar] [CrossRef] 
[4] 邵凤, 郭强, 曾诗奇, 刘建国. 微博系统网络结构的研究进展[J]. 电子科技大学学报, 2014, 43(2): 174-183.
[5] 王江盼, 郭强, 刘建国. 基于PageRank的合著论文中作者贡献分配算法[J]. 电子科技大学学报, 2020, 49(6): 918-923.
[6] 潘侃, 尹春林, 王磊, 陈端兵. 基于特征工程的重要节点挖掘方法[J]. 电子科技大学学报, 2021, 50(6): 930-937.
[7] 曹开臣, 陈明仁, 张千明, 蔡世民, 周涛. 基于网络节点中心性的新闻重要性评价研究[J]. 电子科技大学学报, 2021, 50(2): 285-293.
[8] 刘建国, 任卓明, 郭强, 汪秉宏. 复杂网络中节点重要性排序的研究进展[J]. 物理学报, 2013, 62(17): 9-18.
[9] 任晓龙, 吕琳媛. 网络重要节点排序方法综述[J]. 科学通报, 2014, 59(13): 1175-1197.
[10] Holme, P. (2014) Analyzing Temporal Networks in Social Media. Proceedings of the IEEE, 102, 1922-1933. [Google Scholar] [CrossRef] 
[11] Takaguchi, T., Sato, N., Yano, K. and Masuda, N. (2012) Importance of Individual Events in Temporal Networks. New Journal of Physics, 14, Article ID: 093003. [Google Scholar] [CrossRef] 
[12] Holme, P. and Saramki, J. (2012) Temporal Networks. Physics Reports, 519, 97-125. [Google Scholar] [CrossRef] 
[13] 郭强, 殷冉冉, 刘建国. 基于TOPSIS的时序网络节点重要性研究[J]. 电子科技大学学报, 2019, 48(2): 296-300.
[14] Lü, L., Zhang, Y.C., Yeung, C.H., et al. (2011) Leaders in Social Networks, the Delicious Case. PLOS ONE, 6, e21202. [Google Scholar] [CrossRef] [PubMed]
[15] Grindrod, P., Parsons, M.C., Higham, D.J. and Estrada, E. (2011) Communicability across Evolving Networks. Physical Review E, 83, Article ID: 046120. [Google Scholar] [CrossRef] 
[16] Kim, H. and Anderson, R. (2012) Temporal Node Central-ity in Complex Networks. Physical Review E, 85, Article ID: 026107. [Google Scholar] [CrossRef] 
[17] Taylor, D., Myers, S.A., Clauset, A. and Porter, M.A. (2017) Eigenvector-Based Centrality Measures for Temporal Networks. Multiscale Modeling & Simulation, 15, 537-574. [Google Scholar] [CrossRef] 
[18] 杨剑楠, 刘建国, 郭强. 基于层间相似性的时序网络节点重要性研究[J]. 物理学报, 2018, 67(4): 279-286.
[19] Bi, J., et al. (2021) Temporal Gravity Model for Important Node Identification in Temporal Networks. Chaos Solitons Fractals, 147, Article ID: 110934. [Google Scholar] [CrossRef] 
[20] Bonacich, P. (1972) Factoring and Weighting Approaches to Status Scores and Clique Identification. The Journal of Mathematical Sociology, 2, 113-120. [Google Scholar] [CrossRef] 
[21] Freeman, L.C. (1977) A Set of Measures of Centrality Based on Betweenness. Sociometry, 40, 35-41. [Google Scholar] [CrossRef] 
[22] Sabidussi, G. (1966) The Centrality Index of a Graph. Psychometrika, 31, 581-603. [Google Scholar] [CrossRef] 
[23] Brin, S. and Page, L. (1998) The Anatomy of a Large-Scale Hyper-textual Web Search Engine. Computer Networks, 30, 107-117. [Google Scholar] [CrossRef] 
[24] Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y. and Xu, Y. (2014) Path Problems in Temporal Graphs. Proceedings of the VLDB Endowment, 7, 721-732. [Google Scholar] [CrossRef] 
[25] Latora, V. and Marchiori, M. (2001) Efficient Behavior of Small-World Networks. Physical Review Letters, 87, Article ID: 198701. [Google Scholar] [CrossRef] 
[26] Qu, C., Zhan, X., Wang, G., Wu, J. and Zhang, Z.-K. (2019) Temporal Information Gathering Process for Node Ranking in Time-Varying Networks. Chaos, 29, Article ID: 0331 16. [Google Scholar] [CrossRef] [PubMed]
[27] Kendall, M.G. (1938) A New Measure of Rank Correlation. Biometrika, 30, 81-93. [Google Scholar] [CrossRef] 
[28] Génois, M., Vestergaard, C., Fournet, J., Panisson, A., Bonmarin, I. and Barrat, A. (2015) Data on Face-to-Face Contacts in an Office Building Suggest a Low-Cost Vac-cination Strategy Based on Community Linkers. Network Science, 3, 326-347. [Google Scholar] [CrossRef] 
[29] Génois, M. and Barrat, A. (2018) Can Co-Location Be Used as a Proxy for Face-to-Face Contacts? EPJ Data Science, 7, Article No. 11. [Google Scholar] [CrossRef] 
[30] Leskovec, J., Kleinberg, J. and Faloutsos, C. (2007) Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (TKDD), 1, 2-Es. [Google Scholar] [CrossRef] 

为你推荐