SISP: a new framework for searching the informative subgraph based on PSO

  • Authors:

  • Chen Chen;Guoren Wang;Huilin Liu;Junchang Xin;Ye Yuan

  • Affiliations:

  • Key Laboratory of Medical Image Computing (NEU) Ministry of Education, China & College of ISE, Northeastern University, ShenYang, China;Key Laboratory of Medical Image Computing (NEU) Ministry of Education, China & College of ISE, Northeastern University, ShenYang, China;Key Laboratory of Medical Image Computing (NEU) Ministry of Education, China & College of ISE, Northeastern University, ShenYang, China;Key Laboratory of Medical Image Computing (NEU) Ministry of Education, China & College of ISE, Northeastern University, ShenYang, China;Key Laboratory of Medical Image Computing (NEU) Ministry of Education, China & College of ISE, Northeastern University, ShenYang, China

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

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Abstract

A significant number of applications on graph require the key relations among a group of query nodes. Given a relational graph such as social network or biochemical interaction, an informative subgraph is urgent, which can best explain the relationships among a group of given query nodes. Based on Particle Swarm Optimization (PSO), a new framework of SISP (Searching the Informative Subgraph based on PSO) is proposed. SISP contains three key stages. In the initialization stage, a random spreading method is proposed, which can effectively guarantee the connectivity of the nodes in each particle; In the calculating stage of fitness, a fitness function is designed by incorporating a sign function with the goodness score; In the update stage, the intersection-based particle extension method and rule-based particle compression method are proposed. To evaluate the qualities of returned subgraphs, the appropriate calculating of goodness score is studied. Considering the importance and relevance of a node together, we present the PNR method, which makes the definition of informativeness more reliable and the returned subgraph more satisfying. At last, we present experiments on a real dataset and a synthetic dataset separately. The experimental results confirm that the proposed methods achieve increased accuracy and are efficient for any query set.