Scalable and Effective Negative Sample Generation for Hyperedge Prediction

TL;DR

The Scalable and Effective Negative Sample Generation for Hyperedge Prediction (SEHP) framework, which utilizes diffusion models to tackle challenges of generating high-quality negative samples and effectively captures global patterns.

Abstract

Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems. Traditional methods often face difficulties in generating high-quality negative samples due to the imbalance between positive and negative instances. To address this, we present the Scalable and Effective Negative Sample Generation for Hyperedge Prediction (SEHP) framework, which utilizes diffusion models to tackle these challenges. SEHP employs a boundary-aware loss function that iteratively refines negative samples, moving them closer to decision boundaries to improve classification performance. SEHP samples positive instances to form sub-hypergraphs for scalable batch processing. By using structural information from sub-hypergraphs as conditions within the diffusion process, SEHP effectively captures global patterns. To enhance efficiency, our approach operates directly in latent space, avoiding the need for discrete ID generation and resulting in significant speed improvements while preserving accuracy. Extensive experiments show that SEHP outperforms existing methods in accuracy, efficiency, and scalability, representing a substantial advancement in hyperedge prediction techniques. Our code is available here.

Figures (2)

• Figure 1: Overview of the Conditional Diffusion-Based Framework for Negative Hyperedge Generation in Hyperedge Prediction. The framework consists of three main components: (1) sampling from the full hypergraph to generate positive samples for batch processing; (2) a discriminator module with an encoder, aggregator, and classifier that evaluates candidate hyperedges; (3) a negative hyperedge generator utilizing conditional diffusion and node ID extraction to create challenging negative samples for training. The iterative process updates the model based on the loss function to refine prediction accuracy.
• Figure 2: Framework of the conditional diffusion-based negative hyperedge generation for hyperedge prediction using generated hyperedge embeddings directly. The figure illustrates the modified approach where node ID extraction is bypassed, allowing the generated negative hyperedge representation to be used directly for classification. This streamlined process enhances efficiency by leveraging the continuous embedding from the diffusion model without discrete node selection.