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cs.LG2024
One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs
Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin +1
Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the…
cs.LG2024
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of discip…