Given graphs with millions or billions of vertices and edges, how can we efficiently make inferences based on partial knowledge? Loopy Belief Propagation (LBP) is a graph inference algorithm widely used in various applications including social network analysis, malware detection, recommendation, and image restoration. The algorithm calculates approximate marginal probabilities of vertices in a graph within a linear running time proportional to the number of edges. However, when it comes to real-world graphs with millions or billions of vertices and edges, this cost overwhelms the computing power of a single machine. Moreover, this kind of large-scale graphs does not fit into the memory of a single machine. Although several distributed LBP methods have been proposed, previous works do not consider the properties of real-world graphs, especially the effect of power-law degree distribution on LBP. Therefore, our work focuses on developing a fast and scalable LBP for such large real-world graphs on distributed environment.
In this paper, we propose DLBP, a Distributed Loopy Belief Propagation algorithm which efficiently computes LBP in a distributed manner across multiple machines. By setting the correct convergence criterion and carefully scheduling the computations, DLBP provides up to 10.7x speed up compared to standard distributed LBP. We show that the algorithm demonstrates near-linear scalability with respect to the number of machines as well as the number of edges.
Fast and Scalable Distributed Loopy Belief Propagation on Real-World Graphs
Saehan Jo, Jaemin Yoo, and U Kang
11th ACM International Conference on Web Search and Data Mining (WSDM) 2018, Los Angeles, CA, USA.
The codes used in the paper are available. [Download]