TY - JOUR AU - O'Madadhain, Joshua AU - Hutchins, Jon AU - Smyth, Padhraic T1 - Prediction and ranking algorithms for event-based network data JO - SIGKDD Explor. Newsl. PY - 2005/ VL - 7 IS - 2 SP - 23 EP - 30 UR - http://portal.acm.org/citation.cfm?id=1117458 DO - 10.1145/1117454.1117458 KW - algorithms KW - event KW - prediction KW - ranking KW - toread L1 - SN - N1 - Prediction and ranking algorithms for event-based network data N1 - AB - Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis techniques, such as social network models, often aggregate the relational information from each event into a single static network. In contrast, in this paper we focus on the temporal nature of such data. In particular, we look at the problems of temporal link prediction and node ranking, and describe new methods that illustrate opportunities for data mining and machine learning techniques in this context. Experimental results are discussed for a large set of co-authorship events measured over multiple years, and a large corporate email data set spanning 21 months. ER -