Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Tiehua Zhang, Yuze Liu, Zhishu Shen, Xingjun Ma, Peng Qi, Zhijun Ding, Jiong Jin

TL;DR

This work tackles learning on heterogeneous graphs with high‑order relations by introducing LFH, a dynamic heterogeneous hypergraph framework that jointly constructs type‑specific hyperedges and updates node embeddings via a type‑aware multi‑head attention mechanism. It first generates high‑quality initial embeddings through pairwise fusion, then dynamically builds multiple hyperedges per master node across node types, and finally learns representations by integrating hyperedge embeddings and node features in an end‑to‑end fashion. The unified loss combines supervised objectives with a hyperedge reconstruction term, enabling robust learning in the presence of noise. Empirical results on ACM, DBLP, and IMDB show LFH consistently outperforms homogeneous, heterogeneous, and hypergraph baselines on node classification and link prediction, with notable gains and favorable scalability, underscoring the practical value of modeling heterogeneity and higher‑order relations in graphs.

Abstract

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

TL;DR

This work tackles learning on heterogeneous graphs with high‑order relations by introducing LFH, a dynamic heterogeneous hypergraph framework that jointly constructs type‑specific hyperedges and updates node embeddings via a type‑aware multi‑head attention mechanism. It first generates high‑quality initial embeddings through pairwise fusion, then dynamically builds multiple hyperedges per master node across node types, and finally learns representations by integrating hyperedge embeddings and node features in an end‑to‑end fashion. The unified loss combines supervised objectives with a hyperedge reconstruction term, enabling robust learning in the presence of noise. Empirical results on ACM, DBLP, and IMDB show LFH consistently outperforms homogeneous, heterogeneous, and hypergraph baselines on node classification and link prediction, with notable gains and favorable scalability, underscoring the practical value of modeling heterogeneity and higher‑order relations in graphs.

Abstract

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.
Paper Structure (32 sections, 17 equations, 12 figures, 8 tables, 1 algorithm)

This paper contains 32 sections, 17 equations, 12 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Homogeneous Graph Learning
  4. Heterogeneous Graph Learning
  5. Hypergraph Learning
  6. Preliminaries and problem formulation
  7. Heterogeneous Hypergraph
  8. Initial Node Embedding
  9. Heterogeneous Hyperedge Generation
  10. Dynamic Hypergraph Learning
  11. Hyperedge Embedding Updating
  12. Multi-head Attention Node Embedding Updating
  13. Loss Function
  14. Model Analysis and Discussion
  15. Computational Cost
  16. ...and 17 more sections

Figures (12)

  • Figure 1: An illustrative example of the ACM dataset: (a) Heterogeneous pairwise graph including three node types (Author, Paper, Subject) and four edge types (Write, Written by, Belong to, Contain). (b) A heterogeneous hypergraph that models two implicit data relations: preference for subjects and the relation among the subjects.
  • Figure 2: An overview of our proposed LFH framework.
  • Figure 3: Type-specific multi-head attention on node embedding update.
  • Figure 4: Comparison of F1 on IMDB_ALL.
  • Figure 5: Performance of LFH as a function of $\lambda$ for several values of $\gamma$ for different datasets.
  • ...and 7 more figures

Theorems & Definitions (2)

  • Definition 1
  • Definition 2