Approximate Shortest Distance Computing Using k-Medoids Clustering

Shortest distance query is widely used aspect in large scale networks. Numerous approaches are present in the literature to approximate the distance between two query nodes. Most popular distance approximation approach is landmark embedding scheme. In this technique selection of optimal landmarks is a NP-hard problem. Various heuristics available to locate optimal landmarks include random, degree, closeness centrality, betweenness and eccentricity etc. In this paper, we propose to employ k-medoids clustering based approach to improve distance estimation accuracy over local landmark embedding techniques. In particular, it is observed that global selection of the seed landmarks causes’ large relative error, which is further reduced using local landmark embedding. The efficacy of the proposed approach is analyzed with respect to conventional graph embedding techniques on six large-scale networks. Results express that the proposed landmark selection scheme reduces the shortest distance estimation error considerably. Proposed technique is able to reduce the approximation error of shortest distance by upto 29% with respect to the other graph embedding technique.