ABSTRACT Most of the best performing link prediction ranking measures evaluate the common neighbourhood of a pair of nodes in a network, in order to assess the likelihood of a new link. On the other hand, the same zero rank value is given to node pairs with no common neighbourhood, which usually are a large number of potentially new links, thus resulting in very low quality overall link ranking in terms of average edit distance to the optimal rank. In this paper we introduce a general technique for improving the quality of the ranking of common neighbours-based measures. The proposed method iteratively applies any given ranking measure to the quasi-common neighbours of the node pair. Experiments held on widely accepted datasets show that QCNAA, a quasi-common neighbourhood measure derived from the well know Adamic- Adar (AA), generates rankings which generally improve the ranking quality, while maintaining the prediction capability of the original AA measure.
Alfredo Milani hasn't uploaded this paper.
Create a free Academia account to let Alfredo know you want this paper to be uploaded.
