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Summary of Explaining Graph Neural Networks Via Structure-aware Interaction Index, by Ngoc Bui et al.


Explaining Graph Neural Networks via Structure-aware Interaction Index

by Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying

First submitted to arxiv on: 23 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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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
The paper introduces the Myerson-Taylor interaction index, a novel framework for interpreting black-box machine learning models with structured inputs, such as graph neural networks. Unlike existing Shapley-based approaches, this method internalizes the graph structure and decomposes coalitions into components satisfying a pre-chosen connectivity criterion. The authors propose MAGE (Myerson-Taylor Structure-Aware Graph Explainer), which uses the second-order Myerson-Taylor index to identify key motifs influencing model predictions. The paper proves that the Myerson-Taylor index satisfies five natural axioms accounting for graph structure and high-order interactions among nodes. Extensive experiments on various graph datasets and models demonstrate MAGE’s superior subgraph explanations compared to state-of-the-art methods.
Low GrooveSquid.com (original content) Low Difficulty Summary
Imagine trying to understand why a computer program made a certain decision, but it’s too complicated and hard to see how the pieces fit together. This paper introduces a new way to help explain these decisions by taking into account the relationships between different parts of the information. Instead of just looking at individual pieces, this method looks at how they work together to make a prediction. The authors test their idea on several datasets and show that it works better than other methods for explaining these kinds of predictions.

Keywords

» Artificial intelligence  » Machine learning