Convex combinations of centrality measures

References

• Agresti, A. (2007). An introduction to categorical data analysis. Hoboken, New Jersey: John Wiley & Sons, Inc.
   Google Scholar
• Agresti, A. (2010). Analysis of ordinary categorial data. Hoboken, New Jersey: John Wiley & Sons, Inc.
   Google Scholar
• Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2), 163–177. doi:10.1080/0022250X.2001.9990249
   Web of Science ®Google Scholar
• Buechel, B., & Buskens, V. (2013). The dynamics of closeness and betweenness. The Journal of Mathematical Sociology, 37(3), 159–191. doi:10.1080/0022250X.2011.597011
   Web of Science ®Google Scholar
• Das, K. S., & Pal, M. (2018). Study on centrality measures in social networks: A survey. Social Network Analysis and Mining, 8(13). doi:10.1007/s13278-018-0493-2
   Google Scholar
• Estrada, E. (2007, April). Characterization of topological keystone species local, global and “meso-scale” centralities in food webs. Ecological Complexity, 4(1–2), 48–57. doi:10.1016/j.ecocom.2007.02.018
   Web of Science ®Google Scholar
• Everett, M. G., Sinclair, P., & Dankelmann, P. (2004). Some centrality results new and old. The Journal of Mathematical Sociology, 28(4), 215–227. doi:10.1080/00222500490516671
   Web of Science ®Google Scholar
• Fei, L., Mo, H., & Deng, Y. (2017). A new method to identify influential nodes based on combining of existing centrality measures. Modern Physics Letters B, 31(26), 1750243. doi:10.1142/S0217984917502438
   Web of Science ®Google Scholar
• Freeman, L. C. (1978/1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. doi:10.1016/0378-8733(78)90021-7
   Web of Science ®Google Scholar
• Friedkin, N. E. (1991). Theoretical foundations for centrality measures. American Journal of Sociology, 96(6), 1478–1504. doi:10.1086/229694
   Web of Science ®Google Scholar
• Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. Berlin, Heidelberg: Springer.
   Google Scholar
• Grassi, R., Stefani, S., & Torriero, A. (2007). Some new results on the eigenvector centrality. The Journal of Mathematical Sociology, 31(3), 237–248. doi:10.1080/00222500701373251
   Web of Science ®Google Scholar
• Kourtellis, N., Alahakoon, T., Simha, R., Iamnitchi, A., & Tripathi, R. (2012). Identifying high betweenness centrality nodes in large social networks. Social Network Analysis and Mining, 3(4), 899–914. doi:10.1007/s13278-012-0076-6
   Google Scholar
• Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342–369. doi:10.2307/2393394
   Web of Science ®Google Scholar
• Kunegis, J. (2013). KONECT: The Koblenz network collection. WWW ‘13 Companion: Proceedings of the 22nd International Conference on World Wide Web, 1343–1350. doi:10.1145/2487788.2488173
   Google Scholar
• Leavitt, H. J. (1951). Some effects of certain communication patterns on group performance. The Journal of Abnormal and Social Psychology, 46(1), 38–50. doi:10.1037/h0057189
   PubMed Web of Science ®Google Scholar
• Mahyar, H., Hasheminezhad, R., Ghalebi, E., Nazemian, A., Grosu, R., Movaghar, A., & Rabiee, H. R. (2018). Identifying central nodes for information flow in social networks using compressive sensing. Social Network Analysis and Mining, 8(33). doi:10.1007/s13278-018-0506-1
   Google Scholar
• Munkres, J. R. (1997). Analysis on manifolds. Redwood City, California: Westview Press.
   Google Scholar
• Nathan, E., & Bader, D. A. (2018). Incrementally updating Katz centrality in dynamic graphs. Social Network Analysis and Mining, 8(26). doi:10.1007/s13278-018-0504-3
   Google Scholar
• Newman, M. (2018). Networks. Oxford, United Kingdom: Oxford University Press.
   Google Scholar
• Oldham, S. A., Fulcher, B., Parkes, L., Arnatkevičiūtė, A., Suo, C., & Fornito, A. (2019, July). Consistency and differences between centrality measures across distinct classes of networks. Plos One, 14(7), e0220061. doi:10.1371/journal.pone.0220061
   PubMed Web of Science ®Google Scholar
• Ronqui, J. R. (2015). Analyzing complex networks through correlations in centrality measurements. Journal of Statistical Mechanics: Theory and Experiment 2015.
   PubMed Web of Science ®Google Scholar
• Saxena, A., Gera, R., & Iyengar, S. R. (2019). A heuristic approach to estimate nodes’ closeness rank using the properties of real world networks. Social Network Analysis and Mining, 9(3). doi:10.1007/s13278-018-0545-7
   Google Scholar
• Sinchair, P. (2004). Betweenness centralization for bipartite graphs. The Journal of Mathematical Sociology, 29(1), 25–31. doi:10.1080/00222500590889730
   Web of Science ®Google Scholar
• Singh, R. R., Iyengar, S. R., Chaudhary, S., & Agarwal, M. (2018). An efficient heuristic for betweenness estimation and ordering. Social Network Analysis and Mining, 8(66). doi:10.1007/s13278-018-0542-x
   Google Scholar
• Vigna, S., & Boldi, P. (2014). Axioms for centrality. Internet Mathematics, 10(3), 222–262. doi:10.1080/15427951.2013.865686
   Google Scholar
• Webber, W., Moffat, A., & Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4), 1–38. doi:10.1145/1852102.1852106
   Web of Science ®Google Scholar
• Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4), 452–473. doi:10.1086/jar.33.4.3629752
   Web of Science ®Google Scholar
• Zhang, Y., Bao, Y., Zhao, S., Chen, J., & Tang, J. (2015). Identifying node importance by combining betweenness centrality and Katz centrality. 2015 International Conference on Cloud Computing and Big Data (CCBD), Shanghai, China. 354–357.
   Google Scholar