An Evaluation of Distributed Processing Models for Random Walk-based Link Prediction Algorithms over Social Big Data

The problem of inferring missing relationships between people in online social networks such as Facebook, Google+ and Twitter is currently being given much attention due to its enormous applicability. To this end, link prediction algorithms which operate on graph data have been considered. However, the relentless increase of the size of such networks calls for distributed processing models able to cope with the associated big amounts of data. In this paper, we study the suitability of three models (Fork-Join, Pregel and DPM) for scaling up a common class of such algorithms, i.e. random walk-based. Broadly, Fork-Join and Pregel promote two rather different ways of creating and handling parallel sub-computations, while DPM is a model combining the best of both. Experiments performed with the Twitter graph and two classical random walk-based algorithms named HITS and SALSA show that DPM outperforms Fork-Join and Pregel by [30-40]% and [10-20]% respectively in terms of recommendation time.