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An event-based framework for characterizing the evolution of interaction graphs (2007)
| Citations: | 94 - 3 self |
BibTeX
@TECHREPORT{Asur07anevent-based,
author = {Sitaram Asur and Srinivasan Parthasarathy},
title = {An event-based framework for characterizing the evolution of interaction graphs},
institution = {},
year = {2007}
}
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Abstract
Interaction graphs are ubiquitous in many fields such as bioinformatics, sociology and physical sciences. There have been many studies in the literature targeted at studying and mining these graphs. However, almost all of them have studied these graphs from a static point of view. The study of the evolution of these graphs over time can provide tremendous insight on the behavior of entities, communities and the flow of information among them. In this work, we present an event-based characterization of critical behavioral patterns for temporally varying interaction graphs. We use non-overlapping snapshots of interaction graphs and develop a framework for capturing and identifying interesting events from them. We use these events to characterize complex behavioral patterns of individuals and communities over time. We show how semantic information can be incorporated to reason about community-behavior events. We also demonstrate the application of behavioral patterns for the purposes of modeling evolution, link prediction and influence maximization. Finally, we present a diffusion model for evolving networks, based on our framework.
Keyphrases
interaction graph event-based framework static point tremendous insight many study non-overlapping snapshot complex behavioral pattern interesting event diffusion model community-behavior event influence maximization behavioral pattern event-based characterization many field link prediction physical science semantic information critical behavioral pattern
