Link Prediction Using Top-k Shortest Distances

نویسندگان

  • Andrei Lebedev
  • JooYoung Lee
  • Víctor Rivera
  • Manuel Mazzara
چکیده

In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-k distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.

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تاریخ انتشار 2017