Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion

• URL: http://arxiv.org/abs/2005.04843v6
• Date: Thu, 13 Apr 2023 17:08:28 GMT
• Title: Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
• Authors: Chaoqi Yang, Ruijie Wang, Shuochao Yao, Tarek Abdelzaher
• Abstract summary: We propose a new hypergraph formulation named the emphline expansion (LE) for hypergraphs learning. The proposed emphline expansion makes existing graph learning algorithms compatible with the higher-order structure. We evaluate the proposed line expansion on five hypergraph datasets, the results show that our method beats SOTA baselines by a significant margin.
• Score: 7.933465724913661
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss. To address the problem, this paper treats vertices and hyperedges equally and proposes a new hypergraph formulation named the \emph{line expansion (LE)} for hypergraphs learning. The new expansion bijectively induces a homogeneous structure from the hypergraph by treating vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple graph, the proposed \emph{line expansion} makes existing graph learning algorithms compatible with the higher-order structure and has been proven as a unifying framework for various hypergraph expansions. We evaluate the proposed line expansion on five hypergraph datasets, the results show that our method beats SOTA baselines by a significant margin.

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