This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. To open the black-box of these deep learning models, post …

Graph regression is a fundamental task and has received increasing attention in a wide
range of graph learning tasks. However, the inference process is often not interpretable …

Contrastive Learning (CL) has shown promising performance in collaborative filtering. The
key idea is to use contrastive loss to generate augmentation-invariant embeddings by …

This work studies the explainability of graph neural networks (GNNs), which is important for
the credibility of GNNs in practical usage. Existing mask-based explanation methods mostly …

Graph convolutional networks (GCNs) can quickly and accurately learn graph
representations and have shown powerful performance in many graph learning domains …

Can we model non-Euclidean graphs as pure language or even Euclidean vectors while
retaining their inherent information? The non-Euclidean property have posed a long term …

Graph Neural Networks (GNNs) have become a building block in graph data processing,
with wide applications in critical domains. The growing needs to deploy GNNs in high-stakes …

Understanding the agent's learning process, particularly the factors that contribute to its
success or failure post-training, is crucial for comprehending the rationale behind the agent's …

We investigate the problem of explainability in machine learning. To address this problem,
Feature Attribution Methods (FAMs) measure the contribution of each feature through a …