Graph Representation Learning Beyond Node and Homophily

TL;DR

This paper introduces PairE, an unsupervised graph embedding method that captures both high- and low-frequency signals by using paired nodes, improving performance on diverse graph tasks beyond homophily assumptions.

Contribution

The paper proposes PairE, a novel unsupervised graph embedding approach using paired nodes and a multi-self-supervised autoencoder to better encode diverse graph signals.

Findings

Outperforms state-of-the-art unsupervised methods on benchmark datasets.

Achieves up to 101.1% improvement on edge classification.

Achieves up to 82.5% improvement on node classification.

Abstract

Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks. Their design is apparently against the task-agnostic principle and generally suffers poor performance in tasks, e.g., edge classification, that demands feature signals beyond the node-view and homophily assumption. To condense different feature signals into the embeddings, this paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support node-related and edge-related tasks. Accordingly, a multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality. Our extensive experiments on a diversity of benchmark datasets clearly show that PairE outperforms the unsupervised state-of-the-art baselines, with up to 101.1\% relative improvement on the edge classification tasks that rely on both the high and low-frequency signals in the pair and up to 82.5\% relative performance gain on the node classification tasks.