Publications
Preprint · arXiv · January 1, 2017
Link Prediction using Top-$k$ Shortest Distances
Andrei Lebedev, Jooyoung Lee, Víctor Rivera, Manuel Mazzara
Abstract
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.
Complex Network Analysis TechniquesAdvanced Graph Neural NetworksRecommender Systems and Techniqueslink predictionnode similarity measuresnetwork analysis
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