Machine Learning on Graphs: A Model and Comprehensive Taxonomy

TL;DR

This paper presents a comprehensive taxonomy and a unifying framework called GRAPHEDM that consolidates various graph representation learning methods, bridging the gap between different paradigms like neural networks, embedding, and regularization.

Contribution

The paper introduces a unifying taxonomy and the GRAPHEDM model that integrates multiple graph learning methods into a single framework, facilitating better understanding and future research.

Findings

Fitted over thirty existing methods into the GRAPHEDM framework.

Unified semi-supervised and unsupervised graph learning algorithms.

Provided a solid foundation for understanding graph representation learning.

Abstract

There has been a surge of recent interest in learning representations for graph-structured data. Graph representation learning methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding (such as shallow graph embedding or graph auto-encoders), focuses on learning unsupervised representations of relational structure. The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning. The third, graph neural networks, aims to learn differentiable functions over discrete topologies with arbitrary structure. However, despite the popularity of these areas there has been surprisingly little work on unifying the three paradigms. Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. We propose a comprehensive taxonomy of representation learning methods for graph-structured data, aiming to unify several disparate bodies of work. Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs (e.g. GraphSage, Graph Convolutional Networks, Graph Attention Networks), and unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc) into a single consistent approach. To illustrate the generality of this approach, we fit over thirty existing methods into this framework. We believe that this unifying view both provides a solid foundation for understanding the intuition behind these methods, and enables future research in the area.