Parallel Social Influence Model with Levy Flight Pattern Introduced for Large-Graph Mining on Weibo.com

With a suitable method to rank the user influence in micro-blogging service, we could get influential individuals to make information reach large populations. Here a novel parallel social influence model is proposed to face to these challenges. In this paper, we firstly propose impact factors named Social Network Centricity and Weibo Heat Trend, describe a general algorithm named ActionRank to calculate the user influence based on these factors and the user-weibo behavior graph. Secondly, we introduce the Levy flight pattern into ActionRank, for the random large distance jumping phenomenon and the power-law distribution of the retweet cascade hops on Weibo.com meet its requirement. Thirdly, the parallel ActionRank is proposed with the help of MapReduce for large-scale graphs.Experiment results demonstrate that ActionRank on Levy flight pattern outperforms other algorithms and show the consistency of parallel ActionRank on datasets with sizes ranging from 20M to 1100 M edges.