A new measure for community structures through indirect social connections

2017/12/31 by Roy Cerqueti, Giovanna Ferraro, Antonio Iovanella · 25 citations
Agricultural and Biological Sciences · Computer Science · Mathematics · Physics and Astronomy · #Aggregate (composite) #Artificial intelligence #Cluster analysis #Clustering coefficient #Combinatorics #Community structure #Complex Network Analysis Techniques #Complex network #Computer science #Context (archaeology) #Data mining #Epistemology #Link (geometry) #Mathematics #Meaning (existential) #Measure (data warehouse) #Opinion Dynamics and Social Influence #Plant and animal studies #Theoretical computer science #Topology (electrical circuits) #cs.SI #physics.soc-ph

paper · pdf · doi:10.1016/j.eswa.2018.07.040

published in Expert Systems with Applications 114, 196-209 (Elsevier BV)

arxiv created 2018/06/21 · openalex publication_date 2018/07/19 · arxiv updated 2019/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Based on an expert systems approach, the issue of community detection can be conceptualized as a clustering model for networks. Building upon this further, community structure can be measured through a clustering coefficient, which is generated from the number of existing triangles around the nodes over the number of triangles that can be hypothetically constructed. This paper provides a new definition of the clustering coefficient for weighted networks under a generalized definition of triangles. Specifically, a novel concept of triangles is introduced, based on the assumption that, should the aggregate weight of two arcs be strong enough, a link between the uncommon nodes can be induced. Beyond the intuitive meaning of such generalized triangles in the social context, we also explore the usefulness of them for gaining insights into the topological structure of the underlying network. Empirical experiments on the standard networks of 500 commercial US airports and on the nervous system of the Caenorhabditis elegans support the theoretical framework and allow a comparison between our proposal and the standard definition of clustering coefficient.

Citations

• Korean university life in a network perspective: Dynamics of a large affiliation network
• Triadic closure in two-mode networks: Redefining the global and local clustering coefficients
• Community structure in social and biological networks
• Community detection in complex networks using extremal optimization
• Emergence of scaling in random networks
• Role of network structure and network effects in diffusion of innovations
• Clustering coefficient and community structure of bipartite networks
• Characterization of complex networks: A survey of measurements
• Statistical mechanics of complex networks
• Community detection in graphs
• The Structure and Function of Complex Networks
• The architecture of complex weighted networks
• Friends and neighbors on the Web
• The structure of the nervous system of the nematode Caenorhabditis elegans
• The psychology of interpersonal relations.