방법 비교

	Temporal PageRank	네트워크 확산 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2016	1927 (epidemic roots); network formalization 1990s–2000s
창시자	Rozenshtein, P. & Gionis, A.	Kermack, W. O. & McKendrick, A. G.
유형	Centrality / ranking algorithm for temporal networks	Simulation / analytical model
원전	Rozenshtein, P. & Gionis, A. (2016). Temporal PageRank. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part II, LNCS 9852, pp. 674–689. Springer.	Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.
별칭	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	diffusion on networks, information diffusion, contagion spreading model, network propagation model
관련	6	5
요약	Temporal PageRank extends the classic PageRank algorithm to time-evolving networks by incorporating the recency and ordering of interactions. Edges are weighted by a decay function so that recent contacts contribute more to a node's score than old ones. The result is a dynamic importance ranking that captures who is influential right now, rather than over the entire history of the network.	Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.