sGrapp: Butterfly Approximation in Streaming Graphs

TL;DR

This paper introduces sGrapp, an adaptive streaming algorithm for approximate butterfly counting in bipartite graphs, which is scalable and effective for analyzing complex, real-world graph streams.

Contribution

The paper presents a novel data-driven approach and an adaptive window-based algorithm for efficient butterfly counting in streaming bipartite graphs, addressing scalability issues.

Findings

sGrapp achieves high accuracy in butterfly counting

sGrapp-x offers optimized performance

Experimental results show superior efficiency and accuracy

Abstract

We study the fundamental problem of butterfly (i.e. (2,2)-bicliques) counting in bipartite streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial in understanding the structure of bipartite graphs. This benefits many applications where studying the cohesion in a graph shaped data is of particular interest. Examples include investigating the structure of computational graphs or input graphs to the algorithms, as well as dynamic phenomena and analytic tasks over complex real graphs. Butterfly counting is computationally expensive, and known techniques do not scale to large graphs; the problem is even harder in streaming graphs. In this paper, following a data-driven methodology, we first conduct an empirical analysis to uncover temporal organizing principles of butterflies in real streaming graphs and then we introduce an approximate adaptive window-based algorithm, sGrapp, for counting butterflies as well as its optimized version sGrapp-x. sGrapp is designed to operate efficiently and effectively over any graph stream with any temporal behavior. Experimental studies of sGrapp and sGrapp-x show superior performance in terms of both accuracy and efficiency.