Generating Human Understandable Explanations for Node Embeddings
This addresses the need for interpretability in graph machine learning, particularly for users in domains like social network analysis, though it is incremental as it builds on existing embedding methods.
The paper tackled the problem of making node embeddings interpretable by linking them to human-understandable graph features, introducing the XM framework that modifies existing algorithms to enhance explainability while preserving performance.
Node embedding algorithms produce low-dimensional latent representations of nodes in a graph. These embeddings are often used for downstream tasks, such as node classification and link prediction. In this paper, we investigate the following two questions: (Q1) Can we explain each embedding dimension with human-understandable graph features (e.g. degree, clustering coefficient and PageRank). (Q2) How can we modify existing node embedding algorithms to produce embeddings that can be easily explained by human-understandable graph features? We find that the answer to Q1 is yes and introduce a new framework called XM (short for eXplain eMbedding) to answer Q2. A key aspect of XM involves minimizing the nuclear norm of the generated explanations. We show that by minimizing the nuclear norm, we minimize the lower bound on the entropy of the generated explanations. We test XM on a variety of real-world graphs and show that XM not only preserves the performance of existing node embedding methods, but also enhances their explainability.