Tag: Graph Neural Networks

Researchers have developed a physics-aware graph neural network that detects power grid anomalies hidden by manipulated measurements and deceptive network ...

A new causal reasoning framework called CCMRD uses counterfactual textual interventions, dot-product visual causal intervention, and graph neural networks to ...

Researchers have proven that minimizing graph counterfactual explanations is NP-hard and introduced Local Bounded Search, an algorithm that shrinks graph-based ...

A new survey in Vicinagearth provides the first systematic review of multi-intent spoken language understanding, comparing decoding strategies, modeling approaches, ...

Researchers have unveiled a data-level prompt injection attack that hijacks graph prompt learning systems by injecting a tiny fraction of ...

A new framework called TDiMS rebuilds molecular descriptors around pairs of chemically meaningful substructures, aiming to make predictions of intramolecular ...

A new dual-view graph neural network called HD-SKRG achieves state-of-the-art few-shot molecular property prediction by combining hierarchical atom and functional-group ...

A comprehensive new survey charts the evolution of rumor source detection in social networks, from centrality-based estimators and epidemic diffusion ...

Researchers have developed a memory-augmented self-distillation framework that boosts graph neural network accuracy by 2.5 to 6 percent across benchmark ...

A comprehensive new review finds that quantum graph neural networks deliver real parameter efficiency and task-specific utility, but definitive quantum ...