Identifying influential nodes based on network representation learning in complex networks

Identifying influential nodes is an important topic in many diverse applications, such as accelerating information propagation, controlling rumors and diseases. Many methods have been put forward to identify influential nodes in complex networks, ranging from node centrality to diffusion-based processes. However, most of the previous studies do not take into account overlapping communities in networks. In this paper, we propose an effective method based on network representation learning. The method considers not only the overlapping communities in networks, but also the network structure. Experiments on real-world networks show that the proposed method outperforms many benchmark algorithms and can be used in large-scale networks.

[1]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[2]  Mason A. Porter,et al.  Robust Detection of Dynamic Community Structure in Networks , 2012, Chaos.

[3]  Leandros Tassiulas,et al.  Detecting Influential Spreaders in Complex, Dynamic Networks , 2013, Computer.

[4]  Jun Ma,et al.  Ranking the spreading ability of nodes in complex networks based on local structure , 2014 .

[5]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Qiang Guo,et al.  Ranking the spreading influence in complex networks , 2013, ArXiv.

[7]  Graham Cormode,et al.  Node Classification in Social Networks , 2011, Social Network Data Analytics.

[8]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[9]  An Zeng,et al.  Ranking spreaders by decomposing complex networks , 2012, ArXiv.

[10]  Michalis Vazirgiannis,et al.  Locating influential nodes in complex networks , 2016, Scientific Reports.

[11]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[12]  Duanbing Chen,et al.  The small world yields the most effective information spreading , 2011, ArXiv.

[13]  Jin Xu,et al.  Leaders in communities of real-world networks☆ , 2016 .

[14]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[15]  Duanbing Chen,et al.  Vital nodes identification in complex networks , 2016, ArXiv.

[16]  L. D. Costa,et al.  Identifying the starting point of a spreading process in complex networks. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[18]  Han Zhao,et al.  Identifying influential nodes in complex networks with community structure , 2013, Knowl. Based Syst..

[19]  Rajendra Akerkar,et al.  Knowledge Based Systems , 2017, Encyclopedia of GIS.

[20]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[21]  Sangwook Kim,et al.  Identifying and ranking influential spreaders in complex networks by neighborhood coreness , 2014 .

[22]  Hui Gao,et al.  Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering , 2013, PloS one.

[23]  R. Burt,et al.  Social network analysis: foundations and frontiers on advantage. , 2013, Annual review of psychology.

[24]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[25]  David Krackhardt,et al.  WANTED: A Good Network Theory of Organization@@@Structural Holes: The Social Structure of Competition. , 1995 .

[26]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[27]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[28]  A. Shimbel.  Structural parameters of communication networks , 1953 .

[29]  Claudio Castellano,et al.  Thresholds for epidemic spreading in networks , 2010, Physical review letters.

[30]  Kurt Bryan,et al.  The $25,000,000,000 Eigenvector: The Linear Algebra behind Google , 2006, SIAM Rev..

[31]  Yiping Yao,et al.  Identifying all-around nodes for spreading dynamics in complex networks , 2012 .

[32]  Minyi Guo,et al.  GraphGAN: Graph Representation Learning with Generative Adversarial Nets , 2017, AAAI.

[33]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[34]  Linyuan Lu,et al.  Quantifying the influence of scientists and their publications: Distinguish prestige from popularity , 2011, ArXiv.

[35]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[36]  M. Zelen,et al.  Rethinking centrality: Methods and examples☆ , 1989 .

[37]  Tao Zhou,et al.  The H-index of a network node and its relation to degree and coreness , 2016, Nature Communications.

[38]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[39]  W. Knight.  A Computer Method for Calculating Kendall's Tau with Ungrouped Data , 1966 .

[40]  Robert D. Miewald.  Administrative Science Quarterly , 1981 .

[41]  A. Arenas,et al.  Community detection in complex networks using extremal optimization. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.