HL-index: Fast Reachability Query in Hypergraphs
Peiting Xie, Xiangjun Zai, Yanping Wu, Xiaoyang Wang, Wenjie Zhang, Lu Qin

TL;DR
This paper introduces the HL-index, a novel indexing method for fast max-reachability queries in hypergraphs, enabling efficient analysis of complex group interactions in various real-world applications.
Contribution
The paper presents the HL-index, a new compact index for max-reachability in hypergraphs, along with a fast construction method using relationship detection and neighbor-indexing.
Findings
HL-index significantly improves query efficiency.
The approach scales well to large datasets.
Experimental results validate the method's effectiveness.
Abstract
Reachability in hypergraphs is essential for modeling complex groupwise interactions in real-world applications such as co-authorship, social network, and biological analysis, where relationships go beyond pairwise interactions. In this paper, we introduce the notion of s-reachability, where two vertices are s-reachable if there exists a sequence of hyperedges (i.e., a walk) connecting them, such that each pair of consecutive hyperedges shares at least s vertices. Moreover, we define the max-reachability query as a generalized form of the s-reachability problem, which aims to find the largest value of s that allows one vertex to reach another. To answer max-reachability queries in hypergraphs, we first analyze limitations of the existing vertex-to-vertex and hyperedge-to-hyperedge indexing techniques. We then introduce the HL-index, a compact vertex-to-hyperedge index tailored for the…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Graph Theory and Algorithms · Complex Network Analysis Techniques
