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Temporal-Based Ranking in Heterogeneous Networks

  • Chen Yu
  • Ruidan Li
  • Dezhong Yao
  • Feng Lu
  • Hai Jin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8707)

Abstract

Ranking is a fundamental task for network analysis, benefiting to filter and find valuable information. Time information impacts results in content that is sensitive to trends and events ranking. The current ranking either assumes that user’s interest and concerns remain static and never change over time or focuses on detecting recency information. Meanwhile most prevalent networks like social network are heterogeneous, that composed of multiple types of node and complex reliance structures. In this paper, we propose a general Temporal based Heterogeneous Ranking (TemporalHeteRank) method. We demonstrate that TemporalHeteRank is suitable for heterogeneous networks on the intuition that there is a mutually information balance relationship between different types of nodes that could be reflected on ranking results. We also explore the impact of node temporal feature in ranking, then we use the node life span by carefully investigating the issues of feasibility and generality. The experimental results on sina weibo ranking prove the effectiveness of our proposed approach.

Keywords

Heterogeneous Networks Heterogeneous Ranking Diverse Rank Information Flow Propagation Hotspot Detection 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Chen Yu
    • 1
  • Ruidan Li
    • 1
  • Dezhong Yao
    • 1
  • Feng Lu
    • 1
  • Hai Jin
    • 1
  1. 1.Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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