Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption

This article describes a research paper on unsupervised node representation learning. The focus is on learning node representations without relying on the homophily assumption, which is a common assumption in graph neural networks. The approach is feature-centric, suggesting a focus on the features of the nodes themselves rather than their relationships with neighbors. This is a significant area of research as it addresses a limitation of many existing methods.