Сравнение методов

	Выявление сообществ в ориентированных графах	Взвешенное обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Machine learning
Год появления	2008	2004–2008
Автор метода	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	Newman, M. E. J.; Blondel et al.
Тип	Graph partitioning / modularity optimization	Graph clustering / community detection
Основополагающий источник	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Другие названия	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Связанные	6	6
Сводка	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.	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.