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 userweibo behavior graph. Secondly, we introduce the Levy flight pattern into ActionRank, for the random large distance jumping phenomenon and the powerlaw 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.