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A fast algorithm for predicting links to nodes of interest

  • Chen, Bolun Institute of Information Science and Technology, Yangzhou University, China - College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China - Department of Physics, University of Fribourg, Switzerland
  • Chen, Ling Institute of Information Science and Technology, Yangzhou University, China - National Key Lab of Novel Software Tech, Nanjing University, Nanjing, China
  • Li, Bin Institute of Information Science and Technology, Yangzhou University, China - National Key Lab of Novel Software Tech, Nanjing University, Nanjing, China
    01.02.2016
Published in:
  • Information Sciences. - 2016, vol. 329, p. 552–567
Link prediction
Vertex similarity
Complex networks
English The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/304892
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