Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

GSAT: Graph Structure Attention Networks

Farshad Noravesh, Reza Haffari, Layki Soon, Arghya Pal

TL;DR

GSAT addresses the limited incorporation of graph structure in standard GNNs by leveraging anonymous random walks (ARWs) to produce latent structural representations that guide attention. By extending Graph Attention Networks with ARW-based structural embeddings, GSAT computes edge attentions from structural context and decouples structural attributes from original node features, enabling effective graph classification with a single layer in some cases. The approach is analyzed theoretically and validated empirically on diverse datasets (e.g., MUTAG, PROTEINS, DD, NCI1), showing competitive or improved performance with hierarchical pooling and robust sensitivity analyses of ARW hyperparameters. The work demonstrates that incorporating rich structural information via ARWs can mitigate oversmoothing and improve discrimination in graphs with complex topologies, offering a scalable, structure-aware alternative to existing graph kernels and diffusion-based methods.

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for processing data represented in graph structures, achieving remarkable success across a wide range of applications. However, to further improve the performance on graph classification benchmarks, structural representation of each node that encodes rich local topological information in the neighbourhood of nodes is an important type of feature that is often overlooked in the modeling. The consequence of neglecting the structural information has resulted high number of layers to connect messages from distant nodes which by itself produces other problems such as oversmoothing. In the present paper, we leverage these structural information that are modeled by anonymous random walks (ARWs) and introduce graph structure attention network (GSAT) which is a generalization of graph attention network(GAT) to integrate the original attribute and the structural representation to enforce the model to automatically find patterns for attending to different edges in the node neighbourhood to enrich graph representation. Our experiments show GSAT slightly improves SOTA on some graph classification benchmarks.

GSAT: Graph Structure Attention Networks

TL;DR

GSAT addresses the limited incorporation of graph structure in standard GNNs by leveraging anonymous random walks (ARWs) to produce latent structural representations that guide attention. By extending Graph Attention Networks with ARW-based structural embeddings, GSAT computes edge attentions from structural context and decouples structural attributes from original node features, enabling effective graph classification with a single layer in some cases. The approach is analyzed theoretically and validated empirically on diverse datasets (e.g., MUTAG, PROTEINS, DD, NCI1), showing competitive or improved performance with hierarchical pooling and robust sensitivity analyses of ARW hyperparameters. The work demonstrates that incorporating rich structural information via ARWs can mitigate oversmoothing and improve discrimination in graphs with complex topologies, offering a scalable, structure-aware alternative to existing graph kernels and diffusion-based methods.

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for processing data represented in graph structures, achieving remarkable success across a wide range of applications. However, to further improve the performance on graph classification benchmarks, structural representation of each node that encodes rich local topological information in the neighbourhood of nodes is an important type of feature that is often overlooked in the modeling. The consequence of neglecting the structural information has resulted high number of layers to connect messages from distant nodes which by itself produces other problems such as oversmoothing. In the present paper, we leverage these structural information that are modeled by anonymous random walks (ARWs) and introduce graph structure attention network (GSAT) which is a generalization of graph attention network(GAT) to integrate the original attribute and the structural representation to enforce the model to automatically find patterns for attending to different edges in the node neighbourhood to enrich graph representation. Our experiments show GSAT slightly improves SOTA on some graph classification benchmarks.

Paper Structure

This paper contains 23 sections, 1 theorem, 16 equations, 7 tables.

Table of Contents

  1. Introduction
  2. Main Contributions
  3. Related Work
  4. Encoding Structural Similarity
  5. Graph Kernels
  6. Anonymous Random Walk
  7. Generalized Graph Diffusion
  8. Graph Random Features
  9. Structural Information
  10. Proposed Method
  11. Preliminaries
  12. Problem Formulation
  13. Preprocessing
  14. Sampling Random Walks
  15. Latent Structural Attention
  16. ...and 8 more sections

Key Result

theorem thmcountertheorem

Let $G = (V, E, X)$ be a graph with node set $V$, edge set $E$, and node feature matrix $X \in \mathbb{R}^{|V| \times d}$. A Graph Attention Network (GAT) with global pooling is used to generate a graph-level representation $h_G$ for classification. If the GAT does not incorporate structural graph i leading to misclassification and poor generalization.

Theorems & Definitions (4)

  • definition thmcounterdefinition: Ivanov2018
  • definition thmcounterdefinition
  • theorem thmcountertheorem
  • proof