[Paper Review] HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs

Sunwoo Kim, Shinhwan Kang+4 more|arXiv (Cornell University)|Mar 31, 2024

Text and Document Classification Technologies4 citations

TL;DR

HypeBoy proposes a novel generative self-supervised learning method for hypergraphs based on the hyperedge filling task, which theoretically connects to node classification and enables robust, general-purpose representation learning. It outperforms 16 baselines across 11 benchmark datasets by mitigating representation collapse, non-uniformity, and over-emphasis on proximity.

ABSTRACT

Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances in generative self-supervised learning (SSL) suggest that hypergraph neural networks learned from generative self supervision have the potential to effectively encode the complex hypergraph topology. Designing a generative SSL strategy for hypergraphs, however, is not straightforward. Questions remain with regard to its generative SSL task, connection to downstream tasks, and empirical properties of learned representations. In light of the promises and challenges, we propose a novel generative SSL strategy for hypergraphs. We first formulate a generative SSL task on hypergraphs, hyperedge filling, and highlight its theoretical connection to node classification. Based on the generative SSL task, we propose a hypergraph SSL method, HypeBoy. HypeBoy learns effective general-purpose hypergraph representations, outperforming 16 baseline methods across 11 benchmark datasets.

Motivation & Objective

• To address the lack of effective generative self-supervised learning strategies for hypergraphs, particularly in designing a meaningful pretext task.
• To establish a theoretical link between the proposed generative task and downstream node classification.
• To develop a method that learns general-purpose hypergraph representations with desirable empirical properties, such as avoiding dimensional collapse and representation non-uniformity.
• To ensure computational efficiency despite the exponential growth of potential hyperedges.
• To validate the method’s robustness across diverse hypergraph structures, label splits, and heterogeneous hypergraph settings.

Proposed method

• The paper formulates a new generative self-supervised learning task called hyperedge filling, where the model predicts missing nodes in a hyperedge.
• HypeBoy employs a contrastive learning objective to align representations of positive and negative node pairs derived from the hyperedge filling task.
• It uses a node swapping augmentation strategy to generate diverse negative samples, improving robustness under non-homophilic conditions.
• The method incorporates a dual-contrastive learning mechanism to prevent representation collapse and enhance feature diversity.
• It applies a symmetric contrastive loss function that encourages positive pairs (from the same hyperedge) to be close and negative pairs (from different hyperedges) to be separated.
• The model is pre-trained on the hyperedge filling task and fine-tuned on downstream node classification with labeled data.

Experimental results

Research questions

• RQ1 What generative self-supervised learning task is most suitable for hypergraph representation learning, and how is it theoretically connected to downstream node classification?
• RQ2 How can a hypergraph SSL method be designed to avoid common failure modes such as dimensional collapse, non-uniformity, and over-emphasis on proximity?
• RQ3 How does the proposed method perform across diverse hypergraph datasets, including heterogeneous and non-homophilic graphs?
• RQ4 Can the method generalize effectively under various node label split settings and maintain robustness when homophily is violated?
• RQ5 What are the practical applications of the hyperedge filling task in real-world recommendation systems?

Key findings

• HypeBoy outperforms 16 baseline methods on 11 benchmark hypergraph datasets in node classification, achieving state-of-the-art performance.
• On the Citeseer dataset, HypeBoy achieves 63.1% accuracy (±4.6) with 5 training nodes per class, surpassing the next best method (TriCL at 60.1%).
• In the 10-node-per-class setting, HypeBoy reaches 67.7% accuracy on Citeseer, significantly outperforming UniGCNII (62.7%) and other baselines.
• HypeBoy demonstrates superior robustness under non-homophilic settings, with the smallest performance drop when node homophily is disrupted.
• On the heterogeneous ACM dataset, HypeBoy achieves 41.7% accuracy (±2.6), outperforming all baselines, including HyperGCL (40.8%) and TriCL (40.3%).
• The method maintains strong performance across multiple label-split settings, confirming its generalization beyond specific data splits.