Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations

TL;DR

This paper introduces a novel graph classification method that uses explicit tensor representations of graphs combined with Capsule Networks, achieving competitive results on benchmark datasets.

Contribution

The paper presents a new approach integrating explicit tensorial graph representations with Capsule Networks for improved graph classification.

Findings

Competitive performance on chemical and protein datasets

Effective fixed-size tensor extraction from variable graphs

Limited hyper-parameter tuning yields strong results

Abstract

Graph classification is a significant problem in many scientific domains. It addresses tasks such as the classification of proteins and chemical compounds into categories according to their functions, or chemical and structural properties. In a supervised setting, this problem can be framed as learning the structure, features and relationships between features within a set of labelled graphs and being able to correctly predict the labels or categories of unseen graphs. A significant difficulty in this task arises when attempting to apply established classification algorithms due to the requirement for fixed size matrix or tensor representations of the graphs which may vary greatly in their numbers of nodes and edges. Building on prior work combining explicit tensor representations with a standard image-based classifier, we propose a model to perform graph classification by extracting fixed size tensorial information from each graph in a given set, and using a Capsule Network to perform classification. The graphs we consider here are undirected and with categorical features on the nodes. Using standard benchmarking chemical and protein datasets, we demonstrate that our graph Capsule Network classification model using an explicit tensorial representation of the graphs is competitive with current state of the art graph kernels and graph neural network models despite only limited hyper-parameter searching.