방법 비교

	Temporal PageRank	시간적 사회 연결망 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2016	2000s–2010s
창시자	Rozenshtein, P. & Gionis, A.	Moody, J.; Holme, P.; Saramäki, J.
유형	Centrality / ranking algorithm for temporal networks	Longitudinal network analysis
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.	Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.
별칭	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	TSNA, longitudinal social network analysis, time-varying network analysis, dynamic SNA
관련	6	4
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.	Temporal Social Network Analysis (TSNA) extends classic social network analysis by treating networks as time-varying structures. Rather than aggregating all ties into a single static snapshot, TSNA tracks when ties form, persist, and dissolve, enabling researchers to study how social structures evolve and how dynamic connectivity shapes diffusion, influence, and inequality over time.