UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (2105.00956v1)

Abstract: Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4\% to 88.8\% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at \url{https://github.com/OneForward/UniGNN}.

• The paper presents UniGNN, a framework that generalizes standard GNNs to hypergraphs using a two-stage aggregation process.
• It demonstrates enhanced performance, notably raising DBLP hypernode classification accuracy from 77.4% to 88.8%.
• The work offers theoretical insights by equating its message-passing power with the 1-GWL algorithm for structural distinction.

UniGNN: A Unified Framework for Graph and Hypergraph Neural Networks

The pursuit of enriched representation learning has led to the development of numerous variants of Graph Neural Networks (GNNs). However, an inherent challenge remains in extending these models to hypergraphs, which encapsulate higher-order entity correlations more effectively than conventional graphs. Addressing this challenge, Jing Huang and Jie Yang present the UniGNN framework, a paradigmatic bridge allowing the generalization of standard GNN architectures to hypergraphs, seamlessly incorporating architectural advancements with minimum modifications. This paper's contributions can be dissected across model generalization, performance analysis, and theoretical insights.

UniGNN is constructed to employ a two-stage aggregation process, adaptable enough to encapsulate the graph and hypergraph data structures seamlessly. In doing so, the framework extends several well-established GNN models to the hypergraph domain. These extensions include UniGCN based on Graph Convolutional Networks, UniGAT leveraging Graph Attention Networks, UniGIN, and UniSAGE inspired by the GraphSAGE paradigm. Each of these models showcases the ability to outperform existing state-of-the-art methods significantly, particularly in predictive tasks such as semi-supervised hypernode classification and inductive learning on evolving hypergraphs.

Empirical results affirm UniGNN's superiority across multiple datasets, marked by accuracy improvements over previous models. For instance, for the DBLP dataset, UniGCN raised the accuracy from 77.4% to 88.8% in semi-supervised hypernode classification. This performance leap reveals the efficacy of incorporating detailed hypergraph information compared to predecessors that relied heavily on graph reduction techniques such as clique expansion, as seen in HGNN and HyperGCN, which often overlooked complex internal structures in hypergraphs.

Another notable contribution of UniGNN is the introduction of UniGCNII, a deep hypergraph neural network model leveraging techniques to mitigate over-smoothing—a common ailment in deep GNNs. UniGCNII is architecturally fortified with initial residual connections and identity mappings, proving its efficacy through comparable or superior performance even as the number of layers increases, which typically degrades other models' performance.

On the theoretical front, the paper explores the expressive power of UniGNN models by evaluating them against the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm, a tool for hypergraph isomorphism testing. It establishes that the message-passing capacity of UniGNN matches the theoretical ceiling of the 1-GWL in terms of distinguishing non-isomorphic structures. This insight underscores UniGNN's robustness to preserve structural information across various hierarchical representations inherent in hypergraphs.

Theoretical implications of this work propose fertile grounds for further adjusting and optimizing GNN frameworks for hypergraph structures. UniGNN not only sets a precedent for future architectural designs in this space but also encourages exploration into more comprehensive embeddings that could extend this framework's discriminative prowess. Furthermore, practical applications stand to benefit from nuanced hypergraph models in domains such as recommendation systems and molecular biology, where complex relationship modeling is paramount.

In summary, UniGNN represents a significant stride in the seamless integration of established graph neural network paradigms into hypergraph contexts, achieving enhanced predictive performance and proving robust in retaining structural integrity. Its capacity to unify and extend GNN techniques provides a solid foundation for continued exploration and adoption of hypergraph neural networks in addressing intricate data relationships, paving the way for future advancements within artificial intelligence.