Efficient Top-k s-Biplexes Search over Large Bipartite Graphs

TL;DR

This paper introduces an efficient algorithm for finding the top-$k$ largest $s$-biplexes in large bipartite graphs, addressing a computationally hard problem with practical techniques and extensive experiments.

Contribution

It formulates the top-$k$ $s$-biplex search problem, proves its NP-hardness, and proposes the FastMVBP algorithm with techniques that significantly improve performance on large graphs.

Findings

FastMVBP outperforms benchmark algorithms by up to three orders of magnitude.

The algorithm has a running time of $O^*(eta_s^{d_2})$, with $d_2$ much smaller than the graph size.

Extensive experiments validate the efficiency and scalability of the proposed methods.

Abstract

In a bipartite graph, a subgraph is an $s$-biplex if each vertex of the subgraph is adjacent to all but at most $s$ vertices on the opposite set. The enumeration of $s$-biplexes from a given graph is a fundamental problem in bipartite graph analysis. However, in real-world data engineering, finding all $s$-biplexes is neither necessary nor computationally affordable. A more realistic problem is to identify some of the largest $s$-biplexes from the large input graph. We formulate the problem as the {\em top-$k$ $s$-biplex search (TBS) problem}, which aims to find the top-$k$ maximal $s$-biplexes with the most vertices, where $k$ is an input parameter. We prove that the TBS problem is NP-hard for any fixed $k\ge 1$. Then, we propose a branching algorithm, named MVBP, that breaks the simple $2^n$ enumeration algorithm. Furthermore, from a practical perspective, we investigate three techniques to improve the performance of MVBP: 2-hop decomposition, single-side bounds, and progressive search. Complexity analysis shows that the improved algorithm, named FastMVBP, has a running time $O^*(\gamma_s^{d_2})$, where $\gamma_s<2$, and $d_2$ is a parameter much smaller than the number of vertex in the sparse real-world graphs, e.g. $d_2$ is only $67$ in the AmazonRatings dataset which has more than $3$ million vertices. Finally, we conducted extensive experiments on eight real-world and synthetic datasets to demonstrate the empirical efficiency of the proposed algorithms. In particular, FastMVBP outperforms the benchmark algorithms by up to three orders of magnitude in several instances.