방법 비교

	Temporal PageRank	방향성 페이지랭크
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2016	1998
창시자	Rozenshtein, P. & Gionis, A.	Brin, S. & Page, L.
유형	Centrality / ranking algorithm for temporal networks	Iterative authority-scoring algorithm
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.	Brin, S. & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Proceedings of the 7th International Conference on World Wide Web (WWW7), 107–117. Elsevier.
별칭	TPR, time-aware PageRank, streaming PageRank, dynamic PageRank	PageRank, PR, Google PageRank, directed link analysis
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.	Directed PageRank is a link-based authority scoring algorithm that assigns importance scores to nodes in a directed graph by iteratively redistributing rank through outgoing edges. Introduced by Brin and Page in 1998 as the backbone of Google Search, it measures not just how many in-links a node has but how authoritative the nodes pointing to it are.