Improving Graph Neural Network Interpretability through Conformal-based Graph Sparsification

Core Concepts

CORES, a novel training approach for Graph Neural Networks, jointly identifies the most predictive subgraph and optimizes the graph classification performance, leading to more interpretable predictions.

Abstract

The paper introduces CORES, a novel training framework for Graph Neural Networks (GNNs) that aims to enhance the interpretability of GNN predictions. The key idea is to jointly identify the most predictive subgraph of the input graph and optimize the performance of the graph classification task using only this subgraph. The authors formulate this as a bi-level optimization problem. The inner optimization learns a graph classifier that minimizes the prediction loss on the identified subgraph. The outer optimization uses reinforcement learning to learn a policy that determines which nodes and/or edges to remove from the original graph, without making assumptions about the structure of the predictive subgraph. The reward function for the reinforcement learning component is carefully designed to account for the uncertainty of the classifier, as captured by conformal predictions. This allows the policy to balance between achieving high performance and obtaining a sparse predictive subgraph. The authors evaluate CORES on nine different graph classification datasets and show that it can achieve competitive performance while relying on significantly sparser subgraphs compared to baselines. This leads to more interpretable GNN-based predictions. The paper also provides qualitative results demonstrating the predictive subgraphs discovered by CORES for the MUTAG dataset.

Stats

The number of nodes in the original graphs ranges from 14 to 284, with an average of 39 to 284 nodes per graph. The number of edges in the original graphs ranges from 28 to 1431, with an average of 41 to 1431 edges per graph.

Quotes

"Graph Neural Networks (GNNs) have become a cornerstone in modern machine learning, excelling in diverse domains such as social network analysis, recommender systems and bioinformatics." "Interpretability in this context is closely tied to graph sparsity, implying the use of a minimal set of nodes and edges from the graph for prediction, rather than the entire graph G." "It is crucial to note that interpretability is achieved through sparsity only when the subgraph completely excludes information from the omitted nodes and edges since only then the subgraph, and thus, the explanation is faithful to the model prediction."

Key Insights Distilled From

Improving the interpretability of GNN predictions through conformal-based graph sparsification

by Pablo Sanche... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12356.pdf

Deeper Inquiries

How can CORES be extended to handle dynamic graphs or graphs with evolving structures

CORES can be extended to handle dynamic graphs or graphs with evolving structures by incorporating mechanisms to adapt to changes in the graph over time. One approach could be to introduce a mechanism that dynamically updates the policy and the graph classifier based on incoming data. This could involve retraining the models periodically with new data to ensure they remain effective in capturing the evolving structure of the graph. Additionally, techniques such as online learning or incremental learning could be employed to continuously update the models as new information becomes available. By incorporating these adaptive strategies, CORES can effectively handle dynamic graphs and evolving structures.

What are the potential limitations of the conformal prediction-based reward function, and how could it be further improved

One potential limitation of the conformal prediction-based reward function in CORES is its reliance on the accuracy of the classifier for determining the reward. If the classifier is not well-calibrated or exhibits high uncertainty, it may lead to suboptimal rewards and impact the performance of the policy. To address this limitation, the reward function could be further improved by incorporating additional measures of uncertainty, such as entropy or confidence intervals, to provide a more robust assessment of the classifier's performance. Additionally, exploring alternative reward functions that consider the diversity of predictions or the model's confidence levels could help mitigate the limitations of the conformal prediction-based reward function and enhance the overall performance of CORES.

What insights can be gained by applying CORES to graph-based tasks beyond classification, such as node or link prediction

Applying CORES to graph-based tasks beyond classification, such as node or link prediction, can provide valuable insights into the underlying structure and relationships within the graph. For node prediction tasks, CORES could be adapted to identify the most influential nodes or communities within the graph, shedding light on key players or clusters that drive network dynamics. In the context of link prediction, CORES could help uncover important connections or missing links in the graph, aiding in the prediction of future interactions or relationships. By leveraging the sparsity-inducing capabilities of CORES, these tasks can benefit from more interpretable and efficient predictions, leading to a deeper understanding of graph structures and dynamics.