Monarch: Distributed Butterfly Counting for Large-scale Bipartite Graph

Yuxin Tang, Mangesh Bendre, Mahashweta Das

Abstract: Bipartite graphs are ubiquitous in real-world applications, where butterflies (2x2-bicliques) serve as fundamental building blocks for analyzing graph motifs. While existing wedge aggregation based butterfly computation methods are effective for small-scale graphs, they face significant scalability challenges when processing large-scale bipartite graphs with billions of vertices and edges. In this paper, we present MONARCH, a distributed framework for efficient butterfly computation. Unlike traditional approaches that require expensive two-hop neighbor traversal, MONARCH operates exclusively on first-hop neighbor information, reducing both computation time and communication overhead. This pioneering framework expands the boundary of butterfly computation methods, making it feasible to process bipartite graphs that are orders of magnitude larger. To demonstrate its scalability, we show experimental results on bipartite graph with billions of vertices and edges.