Monitoring Network Evolution using MDL
Ferlez, J.
Faloutsos, C.
Leskovec, J.
Mladenic, D.
Grobelnik, M.
Dept. of Knowledge Technol., Jozef Stefan Inst., Ljubljana;
This paper appears in: Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on
Publication Date: 7-12 April 2008
On page(s): 1328-1330
Location: Cancun,
ISBN: 978-1-4244-1836-7
INSPEC Accession Number: 9963743
Digital Object Identifier: 10.1109/ICDE.2008.4497545
Current Version Published: 2008-04-25
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.
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