Information communities: The network structure of communication |
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| Affiliation: | 1. Warrington College of Business Administration, University of Florida, Gainesville, FL 32611, United States;2. Lubin School of Business, Pace University, New York, NY 10038, United States;3. Haas School of Business, University of California at Berkeley, Berkeley, CA 94720-1900, United States;1. Nonlinear Scientific Research Center, Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu, 212013, PR China;2. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, PR China;1. Sorbonne Universités, UPMC Univ Paris 06, CNRS, Lab. PHENIX, Paris, France;2. Grupo de Fluidos Complexos Inst. de Quimica, Univ. de Brasília, Brasília (DF), Brazil;3. Lab. Léon Brillouin – CE-Saclay, Gif-sur-Yvette, France;4. Dpt de physique, Univ. de Cergy Pontoise, Cergy-Pontoise, France;1. Department of Physics, Fudan University, Shanghai 200433, China;2. School of Computer Science, Fudan University, Shanghai 200433, China;3. Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China;4. Instituto de Fisica, Universidade Federal da Bahia, 40210-210, Salvador, Brazil;1. School of Business, East China University of Science and Technology, Shanghai 200237, China;2. School of Science, East China University of Science and Technology, Shanghai 200237, China;3. Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;4. Institute of Physics, Academia Sinica, Taipei 115, Taiwan;5. Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China |
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| Abstract: | This study puts forward a variable clique overlap model for identifying information communities, or potentially overlapping subgroups of network actors among whom reinforced independent links ensure efficient communication. We posit that the average intensity of communication between related individuals in information communities is greater than in other areas of the network. Empirical tests show that the variable clique overlap model is indeed more effective in identifying groups of individuals that have strong internal relationships in communication networks relative to prior cohesive subgroup models; the pathways generated by such an arrangement of connections are particularly robust against disruptions of information transmission. Our findings extend the scope of network closure effects proposed by other researchers working with communication networks using social network methods and approaches, a tradition which emphasizes ties between organizations, groups, individuals, and the external environment. |
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| Keywords: | Communities Communication Information transmission Network closure |
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