방법 비교

	Directed Network Diffusion Analysis	시간적 네트워크 확산 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927	2012
창시자	Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading)	Holme, P. & Saramäki, J.
유형	Network spreading and cascade analysis	Network 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.	Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
별칭	directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation	TNDA, dynamic network diffusion, time-varying network spreading, diffusion on temporal networks
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.	Temporal Network Diffusion Analysis studies how information, disease, influence, or other contagions spread through networks whose structure changes over time. By modeling edges as time-stamped contacts rather than static links, it captures the critical role of timing and ordering in determining which nodes get reached, how fast, and through which pathways — producing conclusions that static network models systematically miss.