2018/10/24 by Frank Havemann, Jochen Gläser, Michael G. Heinz +1
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Biology #Complex Network Analysis Techniques #Computational biology #Computer network #Computer science #Core (optical fiber) #Ecology #Economic geography #Geography #Identification (biology) #Link (geometry) #Opinion Dynamics and Social Influence #Telecommunications #cs.DL #cs.SI #physics.soc-ph
paper · pdf · doi:10.1007/978-3-030-05411-3_18
published as Complex Networks and Their Applications VII, Studies in Computational Intelligence, pp. 219-230. Springer International Publishing 2019 · 12 pages, 2 figures, submitted version of a paper accepted for the 7th International Conference on Complex Networks and Their Applications, December 11-13, 2018, Cambridge, UK; revised version at http://141.20.126.227/~qm/papers/
arxiv created 2018/10/24 · openalex created_date 2018/10/26 · openalex publication_date 2018/12/01 · arxiv updated 2018/12/07 · openalex updated_date 2026/08/05
Communities in networks are commonly considered as highly cohesive subgraphs which are well separated from the rest of the network. However, cohesion and separation often cannot be maximized at the same time, which is why a compromise is sought by some methods. When a compromise is not suitable for the problem to be solved it might be advantageous to separate the two criteria. In this paper, we explore such an approach by defining communities as well separated subgraphs which can have one or more cohesive cores surrounded by peripheries. We apply this idea to link communities and present an algorithm for constructing hierarchical core-periphery structures in link communities and first test results.