Finding information nebula over large networks

  • Authors:

  • Lijun Chang;Jeffrey Xu Yu;Lu Qin;Yuanyuan Zhu;Haixun Wang

  • Affiliations:

  • The Chinese University of Hong Kong, Hong Kong, China;The Chinese University of Hong Kong, Hong Kong, China;The Chinese University of Hong Kong, Hong Kong, China;The Chinese University of Hong Kong, Hong Kon, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

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Abstract

Social and information networks have been extensively studied over years. In this paper, we concentrate ourselves on a large information network that is composed of entities and relationships, where entities are associated with sets of keyword terms (kterms) to specify what they are, and relationships describe the link structure among entities which can be very complex. Our work is motivated but is different from the existing works that find a best subgraph to describe how user-specified entities are connected. We compute information nebula (cloud) which is a set of top-K kterms P that are most correlated to a set of user-specified kterms Q, over a large information network. Our goal is to find how kterms are correlated given the complex information network among entities. The information nebula computing requests us to take all possible kterms into consideration for the top-K kterms selection, and needs to measure the similarity between kterms by considering all possible subgraphs that connect them instead of the best single one. In this work, we compute information nebula using a global structural-context similarity, and our similarity measure is independent of connection subgraphs. To the best of our knowledge, among the link-based similarity methods, none of the existing work considers similarity between two sets of nodes or two kterms. We propose new algorithms to find top-K kterms P for a given set of kterms Q based on the global structural-context similarity, without computing all the similarity scores of kterms in the large information network. We performed extensive performance studies using large real datasets, and confirmed the effectiveness and efficiency of our approach.