Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (4): 106-116.doi: 10.11896/jsjkx.230300110

• Database & Big Data & Data Science • Previous Articles     Next Articles

Node Influence Ranking Model Based on Transformer

XI Ying, WU Xuemeng, CUI Xiaohui   

  1. Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan 430000,China
  • Received:2023-03-13 Revised:2023-06-29 Online:2024-04-15 Published:2024-04-10
  • Supported by:
    National Key R&D Program of China(2018YFC1604000) and Operation Wise Eyes(G20220126).

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Abstract: Node influence ranking is a key topic in complex networks,and plays an important role in identifying key nodes and measuring node influence.There has been much research exploring node influence based on complex networks,with deep learning shows great potential.However,existing convolutional neural networks(CNNs) and graph neural networks(GNNs) are often based on fixed dimensional features as input and cannot effectively distinguish between neighboring nodes,making them unsuitable for diverse complex networks.In order to solve these problems,a simple and effective node influence ranking model is proposed in this paper.In this model,the input sequence of nodes contains information about the nodes themselves and their neighbors,and the length of the input sequence can be dynamically adjusted according to the network to ensure that the model obtains sufficient information about the nodes.The model also uses the self-attention mechanism to enable nodes to efficiently aggregate information about their neighbors in the input sequence,thus identifying the influence of nodes.Experiments are conducted on 12 real network datasets to verify the effectiveness of the model against seven existing methods using multi-dimensional evaluation criteria.Experimental results show that the model can identify the influence of nodes in complex networks more effectively.

Key words: Transformer, Complex networks, Node influence, SIR, Influence ranking

CLC Number: 

  • TP391
[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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