Eigen-optimization on large graphs by edge manipulation

Chen Chen, Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad, Michalis Faloutsos, Christos Faloutsos

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Large graphs are prevalent in many applications and enable a variety of information dissemination processes, e.g., meme, virus, and influence propagation. How can we optimize the underlying graph structure to affect the outcome of such dissemination processes in a desired way (e.g., stop a virus propagation, facilitate the propagation of a piece of good idea, etc)? Existing research suggests that the leading eigenvalue of the underlying graph is the key metric in determining the so-called epidemic threshold for a variety of dissemination models. In this paper, we study the problem of how to optimally place a set of edges (e.g., edge deletion and edge addition) to optimize the leading eigenvalue of the underlying graph, so that we can guide the dissemination process in a desired way. We propose effective, scalable algorithms for edge deletion and edge addition, respectively. In addition, we reveal the intrinsic relationship between edge deletion and node deletion problems. Experimental results validate the effectiveness and efficiency of the proposed algorithms.

Original languageEnglish (US)
Article number49
JournalACM Transactions on Knowledge Discovery from Data
Volume10
Issue number4
DOIs
StatePublished - Jun 2016

Keywords

  • Edge manipulation
  • Graph mining
  • Immunization
  • Scalability

ASJC Scopus subject areas

  • Computer Science(all)

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    Chen, C., Tong, H., Prakash, B. A., Eliassi-Rad, T., Faloutsos, M., & Faloutsos, C. (2016). Eigen-optimization on large graphs by edge manipulation. ACM Transactions on Knowledge Discovery from Data, 10(4), [49]. https://doi.org/10.1145/2903148