2018/01/31 by Haoye Lu, Amiya Nayak
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Community structure #Complex Network Analysis Techniques #Computer science #Data mining #Distributed computing #Focus (optics) #Graph #Opinion Dynamics and Social Influence #Peer-to-Peer Network Technologies #Phone #Scheme (mathematics) #Set (abstract data type) #Similarity (geometry) #Theoretical computer science #cs.SI #physics.soc-ph
paper · pdf · doi:10.3390/fi11020041
arxiv created 2018/10/14 · openalex publication_date 2019/02/12 · arxiv updated 2019/02/13 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/30
Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, most detection algorithms are derived independently, which is arduous and even unnecessary. Although recent research shows that a general detection method that serves all purposes does not exist, we believe that there is some general procedure of deriving detection algorithms. In this paper, we represent such a general scheme. We mainly focus on two types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a method to define and detect community structures. Finally, we also give a demonstration to show how our design scheme works.