방법 비교

	k-Core Decomposition	커뮤니티 탐지
분야	네트워크 분석	네트워크 분석
계열	Process / pipeline	Process / pipeline
기원 연도	1983	2002–2019 (algorithm family)
창시자	Stephen B. Seidman	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
유형	Graph pruning and hierarchical decomposition	Graph-partitioning / clustering algorithm family
원전	Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
별칭	Core Decomposition, Coreness Decomposition, Shell Decomposition, Çekirdek Ayrıştırma	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
관련	3	5
요약	k-Core Decomposition is a graph-theoretic method that partitions the vertices of a network into a nested sequence of subgraphs called k-cores. A k-core is the maximal subgraph in which every vertex has at least k neighbors within that subgraph. Introduced by Stephen B. Seidman in 1983, the method assigns each vertex a coreness number that captures its structural centrality relative to the local connectivity of the graph.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?