JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2014, Vol. 44 ›› Issue (6): 26-31.doi: 10.6040/j.issn.1672-3961.1.2014.116

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LDA-based link prediction in social network

LU Wenyang, XU Jiayi, YANG Yubin   

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, Jiangsu, China
  • Received:2014-03-31 Revised:2014-11-14 Online:2014-12-20 Published:2014-03-31

Abstract: To address the problem of ignoring the text contents of nodes in social network link prediction methods, a Latent Dirichlet Allocation(LDA)-based collaborative evolutionary link prediction algorithm was proposed. The algorithm used LDA model to analyze the text content and abstracted a topic distribution vector for each node; The product of the topic distribution vectors was adopted to measure the similarity between the nodes' contents; Afterwards, the content similarity matrix was added to the adjacency matrix and the similarities between the nodes were computed consequently; At last, k most similar nodes were selected as the prediction result. The experimental results showed that the proposed algorithm achieved good prediction performance in sparse networks.

Key words: network evolution, social network, link prediction, topic model, Latent Dirichlet Allocation

CLC Number: 

  • TP301
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