방법 비교

	Bayesian PageRank	Temporal PageRank
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	1999 (PageRank); 2000s (Bayesian extension)	2016
창시자	Page, L. & Brin, S. (PageRank); Bayesian extension by multiple authors	Rozenshtein, P. & Gionis, A.
유형	Probabilistic centrality measure	Centrality / ranking algorithm for temporal networks
원전	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.	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.
별칭	Bayesian PR, probabilistic PageRank, uncertainty-aware PageRank, stochastic PageRank	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank
관련	6	6
요약	Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. It is used in web analysis, citation networks, and social network research where rank uncertainty matters.	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.