HyperNATE: Scaling tensor-based hypergraph neural networks through attention

Area of Science:

• Artificial Intelligence
• Graph Neural Networks
• Complex Systems Modeling

Background:

• Hypergraphs model higher-order interactions in complex systems, requiring specialized neural networks.
• Existing Hypergraph Neural Networks (HGNNs), particularly tensor-based ones (t-HGNNs), struggle with computational costs and scalability.
• Many HGNNs assume homophily, limiting their effectiveness on heterophilic datasets where connected nodes have dissimilar features.

Purpose of the Study:

• To introduce a novel neural architecture, HyperNATE, addressing the limitations of current HGNNs.
• To improve the scalability and performance of HGNNs on complex, higher-order interaction data.
• To enhance HGNNs' ability to handle heterophilic graph structures.

Main Methods:

• Developed Hypergraph Neighborhood Aggregation Transformer Encoder (HyperNATE).
• Decoupled computationally intensive tensor-based message aggregation via pre-computation.
• Utilized a transformer encoder with self-attention for parallelized multi-hop neighborhood aggregation.
• Incorporated a high-pass filter to capture discriminative features in heterophilic settings and mitigate oversmoothing.

Main Results:

• HyperNATE achieved training speeds 10-100x faster than existing tensor-based HGNNs (t-HGNNs).
• Demonstrated state-of-the-art performance on node classification benchmarks.
• Showcased strong capabilities on large hypergraphs and heterophilic datasets.

Conclusions:

• HyperNATE offers a scalable and efficient solution for modeling higher-order interactions using hypergraphs.
• The proposed architecture effectively handles heterophily and mitigates oversmoothing issues.
• HyperNATE represents a significant advancement for applying neural networks to complex systems analysis.