On the Interpretability and Evaluation of Graph Representation Learning
This work addresses the need for better interpretability and evaluation in graph machine learning, which is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackles the problem of limited interpretability in graph representation learning by exploring methods to interpret node embeddings and proposing a robust evaluation framework, testing these on graphs with different properties to investigate the relationship between training parameters and embedding performance in recovering graph structure.
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditional graph techniques, they are still perceived as techniques with limited insight into the information encoded in these representations. In this work, we explore methods to interpret node embeddings and propose the creation of a robust evaluation framework for comparing graph representation learning algorithms and hyperparameters. We test our methods on graphs with different properties and investigate the relationship between embedding training parameters and the ability of the produced embedding to recover the structure of the original graph in a downstream task.