Semi-data-driven network coarsening

Li Gao, Jia Wu, Hong Yang, Zhi Qiao, Chuan Zhou*, Yue Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)

Abstract

Network coarsening refers to a new class of graph 'zoom-out' operations by grouping similar nodes and edges together so that a smaller equivalent representation of the graph can be obtained for big network analysis. Existing network coarsening methods consider that network structures are static and thus cannot handle dynamic networks. On the other hand, data-driven approaches can infer dynamic network structures by using network information spreading data. However, existing data-driven approaches neglect static network structures that are potentially useful for inferring big networks. In this paper, we present a new semi-data-driven network coarsening model to learn coarsened networks by embedding both static network structure data and dynamic network information spreading data. We prove that the learning model is convex and the Accelerated Proximal Gradient algorithm is adapted to achieve the global optima. Experiments on both synthetic and real-world data sets demonstrate the quality and effectiveness of the proposed method.

Original languageEnglish
Title of host publicationIJCAI 2016
Subtitle of host publicationProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence
EditorsGerhard Brewka
Place of PublicationPalo Alto, CA
PublisherAssociation for the Advancement of Artificial Intelligence
Pages1483-1489
Number of pages7
ISBN (Electronic)9781577357704, 9781577357711
Publication statusPublished - 2016
Externally publishedYes
EventInternational Joint Conferences on Artificial Intelligence (25th : 2016) - New York, United States
Duration: 9 Jul 201615 Jul 2016

Conference

ConferenceInternational Joint Conferences on Artificial Intelligence (25th : 2016)
CountryUnited States
CityNew York
Period9/07/1615/07/16

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