Router: A Message Passing Model for Large-Scale Graph Mining

Many parallel computational models have been employed in many papers to process the large-scale graph. In this paper, we propose a message passing model Router which could be invoked by most of current parallel computational models to process the large graph. The model is good at solving the multi-source traversal problem which often occurs in many complex graph algorithms. As the model can traverse the graph from different source at the same time, the multi-source traversal will finish in much less iteration than before. In this way, the total time of the algorithm involves multi-source traversal will be reduced in a large scale. Besides, the Router model is flexible enough to express a broad set of algorithms by implementing the Router´s abstract method. Finally, the experiment shows the efficiency and scalability of the model.