방법 비교

	Directed Network Diffusion Analysis	Directed Community Detection
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	2008
창시자	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.
유형	Network spreading and cascade analysis	Graph partitioning / modularity optimization
원전	Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146.	Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
별칭	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning
관련	6	6
요약	Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research.	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.