방법 비교

	Directed Community Detection	모듈성 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2008	2004
창시자	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Newman, M. E. J. & Girvan, M.
유형	Graph partitioning / modularity optimization	Community detection / graph partitioning
원전	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
별칭	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
관련	6	5
요약	Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.