Evaluating link significance in maintaining network connectivity based on link prediction.

Evaluating the significance of nodes or links has always been an important issue in complex networks, and the definition of significance varies with different perspectives. The significance of nodes or links in maintaining the network connectivity is widely discussed due to its application in targeted attacks and immunization. In this paper, inspired by the weak tie phenomenon, we define the links' significance by the dissimilarity of their endpoints. Some link prediction algorithms are introduced to define the dissimilarity of nodes based solely on the network topology. Experiments in synthetic and real networks demonstrate that the method is especially effective in the networks with higher clustering coefficients.

[1]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[2]  Antonio Politi,et al.  Immunization and targeted destruction of networks using explosive percolation , 2016, Physical review letters.

[3]  Huawei Shen,et al.  Bridgeness: a local index on edge significance in maintaining global connectivity , 2010, 1005.2652.

[4]  Olav Tirkkonen,et al.  Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks , 2011, IEEE Transactions on Wireless Communications.

[5]  James Moody,et al.  Peer influence groups: identifying dense clusters in large networks , 2001, Soc. Networks.

[6]  M Leone,et al.  Trading interactions for topology in scale-free networks. , 2005, Physical review letters.

[7]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[8]  Alessandro Vespignani,et al.  Detecting global bridges in networks , 2015, J. Complex Networks.

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

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

[11]  Nino Antulov-Fantulin,et al.  Underestimated cost of targeted attacks on complex networks , 2017, Complex..

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

[13]  Marko Bajec,et al.  Robust network community detection using balanced propagation , 2011, ArXiv.

[14]  F. Harary,et al.  Eccentricity and centrality in networks , 1995 .

[15]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[16]  Hai-Jun Zhou,et al.  Identifying optimal targets of network attack by belief propagation , 2016, Physical review. E.

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

[18]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[20]  Hernán A. Makse,et al.  Influence maximization in complex networks through optimal percolation , 2015, Nature.

[21]  Eva A Enns,et al.  Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches. , 2015, Journal of theoretical biology.

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

[23]  B. Bollobás.  The evolution of random graphs , 1984 .

[24]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

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

[26]  P. Bonacich.  Factoring and weighting approaches to status scores and clique identification , 1972 .

[27]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[28]  Peter Sanders,et al.  Fast Routing in Road Networks with Transit Nodes , 2007, Science.

[29]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Fernando Berzal Galiano,et al.  A Survey of Link Prediction in Complex Networks , 2016, ACM Comput. Surv..

[31]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

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

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

[34]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[35]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[36]  Petter Holme,et al.  Three faces of node importance in network epidemiology: Exact results for small graphs , 2017, Physical review. E.

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

[38]  M E J Newman,et al.  Identity and Search in Social Networks , 2002, Science.