Abstract
In social network (SN), a node is considered as a social entity and a link defines the connection between social entities. In general, the link is shown as a dyadic relationship which is unable to represent a group having super-dyadic relationship. Hypergraph model of a network preserves the super-dyadic relation between the nodes. Several algorithms have been developed to measure the node importance and ranking the nodes according to importance. Some measures take less time, whereas some take more time. We propose a method to find the correlation between the different importance measures in hypergraph. By establishing high correlation, the ranking of a time inefficient importance measure can be computed from a time-efficient measure. In this paper, we present our contribution in twofold. At first, we show the construction of primal/Gaifman graph from hypergraph. Secondly, we establish the correlation between the different importance measures that are used for ranking the nodes of a hypergraph.
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References
Babu, K.S., Jena, S.K., Hota, J., Moharana, B.: Anonymizing social networks: a generalization approach. Comput. Electr. Eng. 39(2013), 1947–1961 (2013)
Bonacich, P., Holdren, A.C., Johnston, M.: Hyper-edges and multidimensional centrality. Social Netw. 26(2004), 189–203 (2004)
Busseniers, E.: General centrality in a hypergraph. arXiv:1403.5162 (2014)
D’Angelo, G., Severini, L., Velaj, Y.: Influence maximization in the independent cascade model. In: ICTCS 2016, Proceedings of the 17th Italian Conference on Theoretical Computer Science, pp. 269–274 (2016)
Faust, K.: Centrality in affiliation networks. Social Netw. 19(1997), 157–191 (1997)
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)
McCulloh, I.: Network topology effects on correlation between centrality measures. Connections 30(1), 21–28 (2010)
Roy, S., Ravindran, B.: Measuring Network Centrality Using Hypergraphs, CoDS’15, pp. 59–68 (2015)
Vogiatzis, D. Influence Study on Hyper-Graphs. Social Networks and Social Contagion. AAAI Technical Report FS-13-05, pp. 24–29 (2013)
Zhao, Q., Lu, H., Gan, Z, Ma, X.: A K-shell decomposition based algorithm for influence maximization. In: Cimiano, P., et al. (eds.) Engineering the Web in the Big Data -Era. ICWE 2015. LNCS, vol. 9114 (2015)
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Mohapatra, D., Patra, M.R. (2021). Rank Consensus Between Importance Measures in Hypergraph Model of Social Network. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_30
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DOI: https://doi.org/10.1007/978-981-15-5788-0_30
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