Approximate Butterfly Counting in Sublinear Time

TL;DR

This paper introduces TLS, a novel sublinear-time sampling algorithm for estimating butterfly counts in large bipartite graphs using limited queries, significantly reducing costs and maintaining accuracy.

Contribution

The paper presents TLS, a practical, theoretically-guaranteed sampling algorithm for butterfly counting in bipartite graphs under a query model, with innovations for variance control and efficiency.

Findings

TLS achieves (1+eps) approximation with sublinear query complexity.

Extensive experiments show TLS reduces query costs and runtime by up to three orders of magnitude.

TLS maintains high accuracy across diverse large bipartite graphs.

Abstract

Bipartite graphs serve as a natural model for representing relationships between two different types of entities. When analyzing bipartite graphs, butterfly counting is a fundamental research problem that aims to count the number of butterflies (i.e., 2x2 bicliques) in a given bipartite graph. While this problem has been extensively studied in the literature, existing algorithms usually necessitate access to a large portion of the entire graph, presenting challenges in real scenarios where graphs are extremely large and I/O costs are expensive. In this paper, we study the butterfly counting problem under the query model, where the following query operations are permitted: degree query, neighbor query, and vertex-pair query. We propose TLS, a practical two-level sampling algorithm that can estimate the butterfly count accurately while accessing only a limited graph structure, achieving significantly lower query costs under the standard query model. TLS also incorporates several key techniques to control the variance, including "small-degree-first sampling" and "wedge sampling via small subsets". To ensure theoretical guarantees, we further introduce two novel techniques: "heavy-light partition" and "guess-and-prove", integrated into TLS. With these techniques, we prove that the algorithm can achieve a (1+eps) accuracy for any given approximation parameter 0 < eps < 1 on general bipartite graphs with a promised time and query complexity. In particular, the promised time is sublinear when the input graph is dense enough. Extensive experiments on 15 datasets demonstrate that TLS delivers robust estimates with up to three orders of magnitude lower query costs and runtime compared to existing solutions.