방법 비교

	Directed Network Diffusion Analysis	사회 연결망 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	1934 (sociometry); 1994 (modern formalization)
창시자	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Moreno, J.L.; formalized by Wasserman & Faust
유형	Network spreading and cascade analysis	Structural/relational analysis framework
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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
별칭	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	SNA, network analysis, sociometric analysis, relational analysis
관련	6	5
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.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.