Interestingness-driven diffusion process summarization in dynamic networks

Qiang Qu, Siyuan Liu, Christian S. Jensen, Feida Zhu, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


The widespread use of social networks enables the rapid diffusion of information, e.g., news, among users in very large communities. It is a substantial challenge to be able to observe and understand such diffusion processes, which may be modeled as networks that are both large and dynamic. A key tool in this regard is data summarization. However, few existing studies aim to summarize graphs/networks for dynamics. Dynamic networks raise new challenges not found in static settings, including time sensitivity and the needs for online interestingness evaluation and summary traceability, which render existing techniques inapplicable. We study the topic of dynamic network summarization: how to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time, and we address the problem by finding interestingness-driven diffusion processes. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose OSNet, an online summarization framework for dynamic networks. We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness and design properties of OSNet.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings
PublisherSpringer Verlag
Number of pages17
EditionPART 2
ISBN (Print)9783662448502
StatePublished - 2014
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy, France
Duration: Sep 15 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8725 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Interestingness-driven diffusion process summarization in dynamic networks'. Together they form a unique fingerprint.

Cite this