PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Monitoring Network Evolution using MDL.
Jure Ferlez, Christos Faloutsos, Jure Leskovec, Dunja Mladenić and Marko Grobelnik
In: ICDE 2008, April 2008, Cancun, Mexico.

Abstract

Given publication titles and authors, what can we say about the evolution of scientific topics and communities over time? Which communities shrunk, which emerged, and which split, over time? And, when in time were the turning points? We propose TimeFall, which can automatically answer these questions given a social network/graph that evolves over time. The main novelty of the proposed approach is that it needs no user-defined parameters, relying instead on the principle of Minimum Description Length (MDL), to extract the communities, and to find good cut-points in time when communities change abruptly: a cut-point is good, if it leads to shorter data description. We illustrate our algorithm on synthetic and large real datasets, and we show that the results of the TimeFall agree with human intuition.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5124
Deposited By:Jan Rupnik
Deposited On:24 March 2009