Journal article

Similarity index based on local paths for link prediction of complex networks

  • Lü, Linyuan Department of Physics, University of Fribourg, Switzerland
  • Jin, Ci-Hang Department of Physics, University of Fribourg, Switzerland
  • Zhou, Tao Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, China
Published in:
  • Physical Review E. - 2009, vol. 80, no. 4, p. 046122
English Predictions of missing links of incomplete networks, such as protein-protein interaction networks or very likely but not yet existent links in evolutionary networks like friendship networks in web society, can be considered as a guideline for further experiments or valuable information for web users. In this paper, we present a local path index to estimate the likelihood of the existence of a link between two nodes. We propose a network model with controllable density and noise strength in generating links, as well as collect data of six real networks. Extensive numerical simulations on both modeled networks and real networks demonstrated the high effectiveness and efficiency of the local path index compared with two well-known and widely used indices: the common neighbors and the Katz index. Indeed, the local path index provides competitively accurate predictions as the Katz index while requires much less CPU time and memory space than the Katz index, which is therefore a strong candidate for potential practical applications in data mining of huge-size networks.
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