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Found 7 papers, 4 papers with code
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
Motivated by this need, we present a benchmarking study on perturbation-based explainability methods for GNNs, aiming to systematically evaluate and compare a wide range of explainability techniques.
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions.
Link Prediction without Graph Neural Networks
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications.
Global Counterfactual Explainer for Graph Neural Networks
One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.
Event Detection on Dynamic Graphs
In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
Feature-based Individual Fairness in k-Clustering
Ensuring fairness in machine learning algorithms is a challenging and essential task.
