Prototype-Based Model-Level Explanations for Graph Neural Networks

Core Concepts

Proposing PAGE, a novel model-level explanation method for GNNs that discovers human-interpretable prototype graphs.

Abstract

The article introduces Prototype-Based GNN-Explainer (PAGE) as a model-level explanation method for graph neural networks. It focuses on explaining what the underlying GNN model has learned for graph classification by discovering prototype graphs. The method outperforms existing explanations both qualitatively and quantitatively using various datasets. It consists of two phases: clustering and selection of graph-level embeddings, and discovery of the prototype graph. The theoretical foundation connecting GNNs to the Weisfeiler-Lehman kernel is discussed, along with empirical validation through visualization of graph-level embeddings. The core prototype search module iteratively finds k-tuples of node indices to construct the final prototype graph candidate.

Stats

"Using six graph classification datasets, we demonstrate that PAGE qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method." "We empirically found that such an update of Yl with a decay factor enables us to avoid searching for the previously selected k-tuples."

Quotes

"The common subgraph pattern that we shall seek is indeed within the training set." "Graph-level embedding vectors revealing common subgraph patterns tend to be co-located on the graph-level embedding space."

Key Insights Distilled From

PAGE

by Yong-Min Shi... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2210.17159.pdf

Deeper Inquiries

How can PAGE's approach to prototype discovery be applied in other domains beyond graph neural networks

PAGE's approach to prototype discovery can be applied in other domains beyond graph neural networks by adapting the methodology to different types of data structures. For instance, in natural language processing, the concept of prototypes could be translated to identifying common patterns or themes in text data. By leveraging embeddings and clustering techniques similar to those used in PAGE, it would be possible to discover representative examples that capture the essence of a particular category or class within textual data. This could aid in understanding how machine learning models make predictions on text-based tasks and provide insights into the underlying decision-making process.

What are potential limitations or criticisms of PAGE's methodology in comparison to existing explanation methods

One potential limitation of PAGE's methodology compared to existing explanation methods is its reliance on node-level and graph-level embeddings for prototype discovery. While this approach may work well for structured data like graphs, it may not generalize as effectively to unstructured or high-dimensional data where extracting meaningful embeddings is more challenging. Additionally, the iterative search process employed by PAGE may lead to increased computational complexity, especially when dealing with large datasets or complex models. Critics might argue that this could hinder scalability and efficiency in real-world applications.

How might understanding the general behavior of pre-trained models benefit applications in real-world scenarios

Understanding the general behavior of pre-trained models can benefit applications in real-world scenarios by enhancing interpretability, robustness, and trustworthiness. In fields such as healthcare or finance where decisions have significant consequences, having insights into how a model arrives at its predictions can help stakeholders validate results and identify potential biases or errors. Moreover, knowledge about a model's overall decision-making process enables practitioners to anticipate performance variations across different datasets or environments, leading to more reliable deployment and improved outcomes in practical settings.