Summary of Generating Human Understandable Explanations For Node Embeddings, by Zohair Shafi et al.
Generating Human Understandable Explanations for Node Embeddings
by Zohair Shafi, Ayan Chatterjee, Tina Eliassi-Rad
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called XM is proposed to improve the explainability of node embedding algorithms. These algorithms produce low-dimensional latent representations of nodes in a graph, which are often used for tasks such as node classification and link prediction. The authors investigate two questions: can each embedding dimension be explained by human-understandable graph features, and how can existing node embedding algorithms be modified to produce explainable embeddings? The answer to the first question is yes, and the authors introduce XM to answer the second question. A key aspect of XM involves minimizing the nuclear norm of generated explanations, which minimizes the lower bound on the entropy of these explanations. The authors demonstrate the effectiveness of XM on various real-world graphs, showing that it not only preserves the performance of existing node embedding methods but also enhances their explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Node embeddings are used to represent nodes in a graph, and this paper proposes a new framework called XM to make them more understandable. Node embeddings are useful for tasks like predicting links between nodes or classifying nodes into categories. The authors ask two questions: can each dimension of the embedding be explained by basic features of the graph, and how can we modify existing node embedding methods to make their results easier to understand? The answer is yes, and XM is designed to help with this. XM works by minimizing a certain measure that helps keep the explanations simple and easy to understand. |
Keywords
» Artificial intelligence » Classification » Embedding
