Activity preserving graph simplification

Francesco Bonchi, Gianmarco Morales, Aristides Gionis, Antti Ukkonen

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

We study the problem of simplifying a given directed graph by keeping a small subset of its arcs. Our goal is to maintain the connectivity required to explain a set of observed traces of information propagation across the graph. Unlike previous work, we do not make any assumption about an underlying model of information propagation. Instead, we approach the task as a combinatorial problem. We prove that the resulting optimization problem is NP -hard. We show that a standard greedy algorithm performs very well in practice, even though it does not have theoretical guarantees. Additionally, if the activity traces have a tree structure, we show that the objective function is supermodular, and experimentally verify that the approach for size-constrained submodular minimization recently proposed by Nagano et al. (28th International Conference on Machine Learning, 2011) produces very good results. Moreover, when applied to the task of reconstructing an unobserved graph, our methods perform comparably to a state-of-the-art algorithm devised specifically for this task.

Original languageEnglish
Pages (from-to)321-343
Number of pages23
JournalData Mining and Knowledge Discovery
Volume27
Issue number3
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes

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Keywords

  • Graph sparsification
  • Information propagation
  • Submodular minimization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications

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