Power genesis in social networks: An entropy-driven decision support model with conditional data

Decision Analytics Journal - Tập 1 - Trang 100003 - 2021
Wilhelm Rödder1, Andreas Dellnitz2, Elmar Reucher3
1Department of Operations Research, FernUniversität in Hagen, 58097 Hagen, Germany
2Chair of Quantitative Methods, Leibniz FH School of Business, 30539 Hannover, Germany
3Department of Business Administration, Private Hochschule für Wirtschaft und Technik, 49377 Vechta, Germany

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Weber, 1980 French, 1959, The bases of social power, 150 Cook, 1983, The distribution of power in exchange networks: Theory and experimental results, Am. J. Sociol., 89, 275, 10.1086/227866 Bonacich, 1987, Power and centrality: A family of measures, Am. J. Sociol., 92, 1170, 10.1086/228631 Bozzo, 2016, A theory on power in networks, Commun. ACM, 59, 75, 10.1145/2934665 Dellnitz, 2020, An entropy-based framework to analyze structural power and power alliances in social networks, Nat. Sci. Rep., 10(1), 1 Can, 2019, A new direction in social network analysis: Online social network analysis problems and applications, Physica A, 535, 10.1016/j.physa.2019.122372 Chen, 2020, A novel method to rank influential nodes in complex networks based on tsallis entropy, Entropy, 22, 10.3390/e22080848 Saura, 2021, Using data sciences in digital marketing: Framework, methods, and performance metrics, J. Innov. Knowl., 6, 92, 10.1016/j.jik.2020.08.001 Restrepo, 2021, Measuring institutional thickness in tourism: An empirical application based on social network analysis, Tour. Manag. Perspect., 37 Ghorbani, 2021, A social-relational approach for analyzing trust and collaboration networks as preconditions for rangeland comanagement, Rangel. Ecol. Manag., 75, 170, 10.1016/j.rama.2020.10.008 Cicalese, 2014, Latency-bounded target set selection in social networks, Theoret. Comput. Sci., 535, 1, 10.1016/j.tcs.2014.02.027 Dreyer, 2009, Irreversible k-threshold processes: Graph-theoretical threshold models of the spread of disease and of opinion, Discrete Appl. Math., 157, 1615, 10.1016/j.dam.2008.09.012 Peng, 2018, Influence analysis in social networks: A survey, J. Netw. Comput. Appl., 106, 17, 10.1016/j.jnca.2018.01.005 Chunaev, 2020, Community detection in node-attributed social networks: A survey, Comp. Sci. Rev., 37 Camacho, 2020, The four dimensions of social network analysis: An overview of research methods, applications, and software tools, Inf. Fusion, 63, 88, 10.1016/j.inffus.2020.05.009 Hafiene, 2020, Influential nodes detection in dynamic social networks: A survey, Expert Syst. Appl., 159, 10.1016/j.eswa.2020.113642 Banerjee, 2020, A survey on influence maximization in a social network, Knowl. Inf. Syst., 62, 3417, 10.1007/s10115-020-01461-4 McClean, 2021, Social network analysis of open source software: A review and categorisation, Inf. Softw. Technol., 130, 10.1016/j.infsof.2020.106442 Zareie, 2021, Minimizing the spread of misinformation in online social networks: A survey, J. Netw. Comput. Appl., 186, 10.1016/j.jnca.2021.103094 2011 Rödder, 2006, Features of the expert-system-shell SPIRIT, Log. J. IGPL, 14, 483, 10.1093/jigpal/jzl020 Csiszar, 1975, i-Divergence geometry of probability distributions and minimization problems, Ann. Probab., 3, 146, 10.1214/aop/1176996454 Skyrms, 1985, Maximum entropy inference as a special case of conditionalization, Synthese, 63, 55, 10.1007/BF00485955 Paris, 1997, In defense of the maximum entropy inference process, Internat. J. Approx. Reason., 17, 77, 10.1016/S0888-613X(97)00014-5 Malvestuto, 2010, Tree and local computations in a cross-entropy minimization problem with marginal constraints, Kybernetika, 46, 621 Beierle, 2015, Relational probabilistic conditionals and their instantiations under maximum entropy semantics for first-order knowledge bases, Entropy, 17, 852, 10.3390/e17020852 Pinski, 2015, Kullback-Leibler Approximation for probability measures on infinite dimensional spaces, SIAM J. Math. Anal., 47, 4091, 10.1137/140962802 Potyka, 2016, An overview of algorithmic approaches to compute optimum entropy distributions in the expert system shell MECore (extended version), J. Appl. Log., 19, 71, 10.1016/j.jal.2016.05.003 Kern-Isberner, 1998, Characterizing the principle of minimum cross-entropy within a conditional-logical framework, Artificial Intelligence, 98, 169, 10.1016/S0004-3702(97)00068-4 Rödder, 2014, Entropy based evaluation of net structures – deployed in social network analysis, Expert Syst. Appl., 41, 7968, 10.1016/j.eswa.2014.06.049 Newcomb, 1961 Brenner, 2017, Compressing strongly connected subgroups in social networks: An entropy-based approach, J. Math. Sociol., 41, 84, 10.1080/0022250X.2017.1284070 Rödder, 2021, Liberté, Égalité, Fraternité – a power study in signed networks, SSRN, 1 Newman, 2012 Jansen, 2006 Rödder, 2003, From information to probability: An axiomatic approach—Inference is information processing, Int. J. Intell. Syst., 18, 383, 10.1002/int.10094 Topsøe, 1974 Roman, 1997 Zegler, 1975