Training-Free Message Passing for Learning on Hypergraphs

2024-02-08

AI Summary

Existing hypergraph neural networks (HNNs) suffer from high computational overhead and deployment costs due to parameterized, training-dependent message passing. Method: We propose TF-MP-Module, a training-free message passing module that decouples structural information extraction—performed offline during preprocessing—from learnable parameter optimization, yielding the efficient TF-HNN framework. Contribution/Results: Our approach introduces the first training-free message passing mechanism, featuring analytical aggregation based on hypergraph adjacency, parameterless propagation operators, and long-range robust propagation. We theoretically establish its information completeness, resistance to over-smoothing, and training efficiency, while fully decoupling structural modeling from parameter learning. Evaluated on seven real-world hypergraph benchmarks, TF-HNN achieves state-of-the-art performance: +10% node classification accuracy on Trivago and reduces training time to just 1% of baseline methods.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node classification. However, the message passing module in existing HNNs typically requires a computationally intensive training process, which limits their practical use. To tackle this challenge, we propose an alternative approach by decoupling the usage of hypergraph structural information from the model learning stage. This leads to a novel training-free message passing module, named TF-MP-Module, which can be precomputed in the data preprocessing stage, thereby reducing the computational burden. We refer to the hypergraph neural network equipped with our TF-MP-Module as TF-HNN. We theoretically support the efficiency and effectiveness of TF-HNN by showing that: 1) It is more training-efficient compared to existing HNNs; 2) It utilises as much information as existing HNNs for node feature generation; and 3) It is robust against the oversmoothing issue while using long-range interactions. Experiments based on seven real-world hypergraph benchmarks in node classification and hyperlink prediction show that, compared to state-of-the-art HNNs, TF-HNN exhibits both competitive performance and superior training efficiency. Specifically, on the large-scale benchmark, Trivago, TF-HNN outperforms the node classification accuracy of the best baseline by 10% with just 1% of the training time of that baseline.

Problem

• Reduces computational burden in hypergraph neural networks
• Eliminates need for training in message passing module
• Improves efficiency and robustness in node classification

Innovation

• Training-free message passing module for hypergraphs
• Decouples hypergraph structure from model learning
• Precomputes structural information, reducing computational burden