Finding Relevant Papers Based on Citation Relations

  • Yicong Liang
  • Qing Li
  • Tieyun Qian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)


With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques.


Paper Relevance Citation Relation Citation Network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yicong Liang
    • 1
  • Qing Li
    • 2
  • Tieyun Qian
    • 3
  1. 1.Department of Computer ScienceCity University of Hong KongHong KongChina
  2. 2.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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