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Prediction and ranking algorithms for event-based network data

Authors:

Joshua O'Madadhain,

Padhraic SmythAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 7, Issue 2

Pages 23 - 30

https://doi.org/10.1145/1117454.1117458

Published: 01 December 2005 Publication History

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.

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Prediction and ranking algorithms for event-based network data

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Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 7, Issue 2

December 2005

152 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1117454

Issue’s Table of Contents

Copyright © 2005 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2005

Published in SIGKDD Volume 7, Issue 2

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