计算机科学 ›› 2024, Vol. 51 ›› Issue (4): 106-116.doi: 10.11896/jsjkx.230300110
席颖, 邬学猛, 崔晓晖
XI Ying, WU Xuemeng, CUI Xiaohui
摘要: 节点影响力排序是复杂网络的一个重点话题,对识别关键节点和衡量节点影响力有着重要作用。目前,已有诸多研究基于复杂网络探索节点影响力,其中深度学习显示出了巨大的潜力。然而,现有卷积神经网络(CNNs) 和图神经网络(GNNs) 模型的输入往往基于固定维度特征,且不能有效地区分邻居节点,无法适应多样性的复杂网络。为了解决上述问题,文中提出了一种简单且有效的节点影响力排序模型。该模型中,节点的输入序列包含节点本身及其邻居节点的信息,且可以根据网络动态调整输入序列长度,确保模型获取到足量的节点信息。同时该模型利用自注意力机制,使节点可以有效地聚合输入序列中邻居节点的信息,从而全面地识别节点的影响力。在12个真实网络数据集上进行实验,通过多维度的评价标准验证了该模型相比7种已有方法的有效性。实验结果表明,在不同的网络结构中,该模型均能有效地识别网络中节点的影响力。
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[1]NEWMAN M E J.The structure and function of complex networks [J].Siam Review,2003,45(2):167-256. [2]LIU Y,SONG A,SHAN X,et al.Identifying critical nodes in power networks:A group-driven framework [J].Expert Systems with Applications,2022,196:116557. [3]XU W X,DONG Y F,GUAN J H,et al.Identifying essential proteins from protein-protein interaction networks based on influence maximization [J].Bmc Bioinformatics,2022,23(SUPPL 8):12. [4]PRIYANTA S,TRISNA I N P.Social Network Analysis ofTwitter to Identify Issuer of Topic using PageRank [J].International Journal of Advanced Computer Science and Applications,2019,10(1):107-111. [5]BONACICH P.Factoring and Weighting Approaches to StatusScores and Clique Identification[J].Journal of Mathematical Sociology,1972,2(1):113-120. [6]BONACICH P.Some unique properties of eigenvector centrality [J].Social Networks,2007,29(4):555-564. [7]SABIDUSSI G.The centrality index of a graph [J].Psy-chometrika,1966,31(4):581-603. [8]FREEMAN L C.A set of measures of centrality based on betweenness [J].Sociometry,1977,40(1):35-41. [9]ZHAO G,JIA P,HUANG C,et al.A machine learning based framework for identifying influential nodes in complex networks [J].IEEE Access,2020,8:65462-65471. [10]WEN X,TU C,WU M,et al.Fast ranking nodes importance in complex networks based on LS-SVM method [J].Physica A:Statistical Mechanics and its Applications,2018,506:11-23. [11]ZHAO G,JIA P,ZHOU A,et al.InfGCN:Identifying influential nodes in complex networks with graph convolutional networks [J].Neurocomputing,2020,414:18-26. [12]ZHANG M,WANG X,JIN L,et al.A new approach for evaluating node importance in complex networks via deep learning methods [J].Neurocomputing,2022,497:13-27. [13]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need [C]//Advances in Neural Information Processing Systems.2017:5998-6008. [14]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding [J].arXiv:1810.04805,2018. [15]SELVA J,JOHANSEN A S,ESCALERA S,et al.Video transformers:A survey [J].arXiv:2201.05991,2022. [16]ZHANG P,WANG J,LI X,et al.Clustering coefficient andcommunity structure of bipartite networks [J].Physica A:Statistical Mechanics and its Applications,2008,387(27):6869-6875. [17]HIRSCH J E.An index to quantify an individual's scientific research output [J].Proceedings of the National Academy of Sciences,2005,102(46):16569-16572. [18]LU P,ZHANG Z.Critical nodes identification in complex networks via similarity coefficient [J].Modern Physics Letters B,2022,36(9):2150620. [19]YANG Y,WANG X,CHEN Y,et al.A novel centrality of in-fluential nodes identification in complex networks [J].IEEE Access,2020,8:58742-58751. [20]KITSAK M,GALLOS L K,HAVLIN S,et al.Identification of influential spreaders in complex networks [J].Nature Physics,2010,6(11):888-893. [21]LIU Y,TANG M,ZHOU T,et al.Improving the accuracy of the k-shell method by removing redundant links:From a perspective of spreading dynamics [J].Scientific reports,2015,5(1):1-11. [22]YUAN H L,FENG C.Ranking and Recognition of Influential Nodes Based on k-shell Entropy [J].Computer Science,2022,49(S2):226-230. [23]KATZ L.A new status index derived from sociometric analysis [J].Psychometrika,1953,18(1):39-43. [24]FEI L,ZHANG Q,DENG Y.Identifying influential nodes incomplex networks based on the inverse-square law [J].Physica A:Statistical Mechanics and its Applications,2018,512:1044-1059. [25]HU J,DU Y,MO H,et al.A modified weighted TOPSIS toidentify influential nodes in complex networks [J].Physica A:Statistical Mechanics and its Applications,2016,444:73-85. [26]CHEN X,TAN M,ZHAO J,et al.Identifying influential nodes in complex networks based on a spreading influence related centrality [J].Physica A:Statistical Mechanics and its Applications,2019,536:122481. [27]DUAN S R,YIN M J,LIU F L,et al.Nodes' Ranking Model Based on Influence Prediction [J].Computer Science,2023,50(3):155-163. [28]YU E Y,WANG Y P,FU Y,et al.Identifying critical nodes in complex networks via graph convolutional networks [J].Knowledge-Based Systems,2020,198:105893. [29]OU Y,GUO Q,XING J L,et al.Identification of spreading influence nodes via multi-level structural attributes based on the graph convolutional network [J].Expert Systems with Applications,2022,203:14. [30]KUMAR S,MALLIK A,KHETARPAL A,et al.Influencemaximization in social networks using graph embedding and graph neural network [J].Information Sciences,2022,607:1617-1636. [31]KUMAR S,MALLIK A,PANDA B S.Influence maximization in social networks using transfer learning via graph-based LSTM [J].Expert Systems with Applications,2023,212:16. [32]MORENO Y,PASTOR-SATORRAS R,VESPIGNANI A.Epidemic outbreaks in complex heterogeneous networks [J].The European Physical Journal B-Condensed Matter and Complex Systems,2002,26:521-529. [33]BAE J,KIM S.Identifying and ranking influential spreaders in complex networks by neighborhood coreness [J].Physica A:Statistical Mechanics and its Applications,2014,395:549-559. [34]KENDALL M G.The treatment of ties in ranking problems[J].Biometrika,1945,33(3):239-251. [35]WEBBER W,MOFFAT A,ZOBEL J.A Similarity Measure for Indefinite Rankings [J].Acm Transactions on Information Systems,2010,28(4):38. [36]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks [J].arXiv:1609.02907,2016. [37]HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs [C]//Advances in Neural Information Processing Systems.2017:1024-1034. [38]VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017. |
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