Parallel Peeling of Bipartite Networks for Hierarchical Dense Subgraph Discovery

Abstract

Wing and Tip decomposition construct a hierarchy of butterfly-dense edge and vertex induced bipartite subgraphs, respectively. They have applications in several domains including e-commerce, recommendation systems and document analysis. Existing decomposition algorithms use a bottom-up approach that constructs the hierarchy in an increasing order of subgraph density. They iteratively peel the entities with minimum butterfly count i.e. remove them from the graph and update the butterfly count of other entities. However, the amount of butterflies in large bipartite graphs makes bottom-up peeling computationally demanding. Furthermore, the strict order of peeling entities results in a numerous sequentially dependent iterations, which makes parallelization challenging. In this paper, we propose a novel Parallel Bipartite Network peelinG (PBNG) framework which adopts a two-phased peeling approach to relax the order of peeling, and in turn, reduce synchronization. The first phase divides the decomposition hierarchy into several partitions using very few peeling iterations. The second phase concurrently processes these partitions to generate the final hierarchy, and requires no global synchronization. The two-phased peeling further enables batching optimizations that dramatically improve the computational efficiency. We empirically evaluate PBNG using several real-world bipartite graphs. Compared to the state-of-the-art frameworks and decomposition algorithms, PBNG achieves up to four orders of magnitude reduction in synchronization and two orders of magnitude speedup, respectively. We also present the first decomposition results of some of the largest public real-world datasets, which PBNG can peel in few minutes but existing algorithms fail to process even in several days.