A PageRank-Based Heuristic Algorithm for Influence Maximization in the Social Network

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 157)

Abstract

The influence maximization is the problem of how to find a small subset of nodes (seed nodes) that could maximize the spread of influence in social network. However,it proved to be NP-hard.We propose a new heuristic algorithm, the High-PageRank greedy algorithm(HPR_Greedy),which searches the seed nodes in a small portion containing only the high-PageRank nodes, based on the power-law influence distribution in non-uniform networks. The experimental results showed that, compared with classical algorithms, the HPR_Greedy algorithm reduced search time and achieved better scalability without losing influence.

Keywords

Greedy Algorithm Social Networking Site Submodular Function Bipartite Network Linear Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhi-Lin Luo
    • 1
  • Wan-Dong Cai
    • 1
  • Yong-Jun Li
    • 1
  • Dong Peng
    • 1
  1. 1.School of Compute Science Northwestern Polytechnic UniversityXi’anChina

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