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Found 10 papers, 2 papers with code
Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance
However, the factors which govern gradient-based editing are understudied, obscuring why edges are chosen and if edits are grounded in an edge's importance.
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
We ground the practical implications of this work through granular analysis on five real-world datasets with varying global homophily levels, demonstrating that (a) GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, and (b) high local homophily does not necessarily confer high performance for a node.
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity, and local assortativity, i. e., the tendency of linked nodes to have similar attributes.
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning
Existing knowledge integration approaches are limited to using differentiable knowledge source to be compatible with first-order DL training paradigm.
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
While edge deletion is a common method used to promote fairness in GNNs, it fails to consider when data is inherently missing fair connections.
Reliable Graph Neural Network Explanations Through Adversarial Training
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection.
How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications
We bridge two research directions on graph neural networks (GNNs), by formalizing the relation between heterophily of node labels (i. e., connected nodes tend to have dissimilar labels) and the robustness of GNNs to adversarial attacks.
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Actionable Attribution Maps for Scientific Machine Learning
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Generative Counterfactual Introspection for Explainable Deep Learning
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation.
