Parallel Algorithms and Heuristics for Efficient Computation of High-Order Line Graphs of Hypergraphs

TL;DR

This paper introduces parallel algorithms and heuristics for efficiently computing high-order line graphs of hypergraphs, enabling better analysis of multi-way interactions in complex systems.

Contribution

The paper presents novel parallel algorithms and heuristics that significantly accelerate the computation of s-overlap and line graphs of hypergraphs, surpassing naive methods.

Findings

Parallel algorithms are over 10 times faster than naive methods.

Heuristics reduce redundant computations and improve performance.

Algorithms perform well especially with large s values.

Abstract

This paper considers structures of systems beyond dyadic (pairwise) interactions and investigates mathematical modeling of multi-way interactions and connections as hypergraphs, where captured relationships among system entities are set-valued. To date, in most situations, entities in a hypergraph are considered connected as long as there is at least one common "neighbor". However, minimal commonality sometimes discards the "strength" of connections and interactions among groups. To this end, considering the "width" of a connection, referred to as the $s$-overlap of neighbors, provides more meaningful insights into how closely the communities or entities interact with each other. In addition, $s$-overlap computation is the fundamental kernel to construct the line graph of a hypergraph, a low-order approximation of the hypergraph which can carry significant information about the original hypergraph. Subsequent stages of a data analytics pipeline then can apply highly-tuned graph algorithms on the line graph to reveal important features. Given a hypergraph, computing the $s$-overlaps by exhaustively considering all pairwise entities can be computationally prohibitive. To tackle this challenge, we develop efficient algorithms to compute $s$-overlaps and the corresponding line graph of a hypergraph. We propose several heuristics to avoid execution of redundant work and improve performance of the $s$-overlap computation. Our parallel algorithm, combined with these heuristics, is orders of magnitude (more than $10\times$) faster than the naive algorithm in all cases and the SpGEMM algorithm with filtration in most cases (especially with large $s$ value).