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Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation

Khaled Mohammed Saifuddin, Mehmet Emin Aktas, Esra Akbas

TL;DR

This work designs a simple yet effective structural and spatial encoding module to incorporate the topological and spatial information of the nodes into their representation, and presents a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints.

Abstract

Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remarkable performance in graph representation learning, their extension to hypergraphs encounters challenges due to their intricate structures. Furthermore, current hypergraph transformers, a special variant of GNN, utilize semantic feature-based self-attention, ignoring topological attributes of nodes and hyperedges. To address these challenges, we propose a Topology-guided Hypergraph Transformer Network (THTN). In this model, we first formulate a hypergraph from a graph while retaining its structural essence to learn higher-order relations within the graph. Then, we design a simple yet effective structural and spatial encoding module to incorporate the topological and spatial information of the nodes into their representation. Further, we present a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints. By leveraging these two modules, THTN crafts an improved node representation, capturing both local and global topological expressions. Extensive experiments conducted on node classification tasks demonstrate that the performance of the proposed model consistently exceeds that of the existing approaches.

Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation

TL;DR

This work designs a simple yet effective structural and spatial encoding module to incorporate the topological and spatial information of the nodes into their representation, and presents a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints.

Abstract

Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remarkable performance in graph representation learning, their extension to hypergraphs encounters challenges due to their intricate structures. Furthermore, current hypergraph transformers, a special variant of GNN, utilize semantic feature-based self-attention, ignoring topological attributes of nodes and hyperedges. To address these challenges, we propose a Topology-guided Hypergraph Transformer Network (THTN). In this model, we first formulate a hypergraph from a graph while retaining its structural essence to learn higher-order relations within the graph. Then, we design a simple yet effective structural and spatial encoding module to incorporate the topological and spatial information of the nodes into their representation. Further, we present a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints. By leveraging these two modules, THTN crafts an improved node representation, capturing both local and global topological expressions. Extensive experiments conducted on node classification tasks demonstrate that the performance of the proposed model consistently exceeds that of the existing approaches.
Paper Structure (22 sections, 18 equations, 3 figures, 5 tables, 1 algorithm)

This paper contains 22 sections, 18 equations, 3 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Preliminaries
  4. Transformer
  5. Hypergraph
  6. Related Work
  7. Methodology
  8. Node Feature Enrichment via Structural and Spatial Encoding
  9. Local Structure Encoding
  10. Centrality Encoding
  11. Uniqueness Encoding
  12. Position Encoding
  13. Structure-Aware Attention
  14. Local Structure-Aware Node-to-Hyperedge Level Attention.
  15. Global Structure-Aware Hyperedge-to-Node Level Attention.
  16. ...and 7 more sections

Figures (3)

  • Figure 1: Topology-guided Hypergraph Transformer Network, THTN, consists of a) Structural and Spatial Encoding that enrich initial node representation via learnable structural and spatial node features and b) Structure-Aware Attention that enables the integration of structural importance of nodes for a hyperedge and hyperedges for a node into regular attribute-based semantic attention to derive the ultimate node representations.
  • Figure 2: Hypergraph Construction: Each community is represented as a hyperedge.
  • Figure 3: The performance (accuracy) of THTN with different numbers of global nodes ($n_g$).

Theorems & Definitions (3)

  • Definition 1
  • Definition 2: Node local clustering coefficient
  • Definition 3: Hyperedge clustering coefficient