Vertex centric asynchronous belief propagation algorithm for large-scale graphs

Inference problems on networks and their algorithms were always important subjects, but more so now with so much data available and so little time to make sense of it. Common applications range from product recommendation to social networks and protein interaction. One of the main inferences in this types of networks is the guilty-by-association method, where labeled nodes propagate their information throughout the network, towards unlabeled nodes. While there is a widely used algorithm for this context, called Belief Propagation, it lacks the necessary convergence guarantees for loopy-networks. More recently, a new alternative method was proposed, called LinBP and while it solved the convergence issue, the scalability for large graphs that do not fit memory remains a challenge. Additionally, most works that try to use BP considering large scale graphs rely on specific infrastructure such as supercomputers and computational clusters. Therefore we propose a new algorithm, that leverages state-of-the-art asynchronous vertex-centric parallel processing techniques in conjunction with the state-of-the-art BP alternative LinBP, to provide a scalable framework for large graph inference that runs on a single commodity machine. Our results show that our algorithm is up to 200 times faster than LinBP's SQL implementation on tested networks, while achieving the same accuracy rate. We also show that due to the asynchronous processing, our algorithm actually needs less iterations to converge when compared to LinBP when using the same parameters. Finally, we believe that our methodology highlights the yet not fully explored parallelism available on commodity machines, leaning towards a more cost-efficient computational paradigm.