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.

@article{1117458, author = {O'Madadhain, Joshua and Hutchins, Jon and Smyth, Padhraic}, title = {Prediction and ranking algorithms for event-based network data}, journal = {SIGKDD Explor. Newsl.}, publisher = {ACM}, address = {New York, NY, USA}, year = {2005}, volume = {7}, number = {2}, pages = {23--30}, url = {http://portal.acm.org/citation.cfm?id=1117458}, doi = {10.1145/1117454.1117458}, issn = {1931-0145}, keywords = {algorithms, event, prediction, ranking, toread}, abstract = {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.} }

%0 = article %A = O'Madadhain, Joshua and Hutchins, Jon and Smyth, Padhraic %C = New York, NY, USA %D = 2005 %I = ACM %T = Prediction and ranking algorithms for event-based network data %U = http://portal.acm.org/citation.cfm?id=1117458