As time goes by: Discovering eras in evolving social networks

Michele Berlingerio, Michele Coscia, Fosca Giannotti, Anna Monreale, Dino Pedreschi

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages81-90
Number of pages10
Volume6118 LNAI
EditionPART 1
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India
Duration: 21 Jun 201024 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6118 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Country/TerritoryIndia
CityHyderabad
Period21/06/1024/06/10

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