Semi-supervised Hypergraph Node Classification on Hypergraph Line Expansion
This addresses the challenge of effectively applying graph learning algorithms to hypergraphs for researchers in machine learning and data science, though it is incremental as it builds on existing expansion methods.
The paper tackled the problem of information loss in hypergraph expansions by proposing a new symmetric formulation called line expansion, which treats vertices and hyperedges equally and reduces hypergraphs to simple graphs, and the results showed that it beats state-of-the-art baselines by a significant margin on five datasets.
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.