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Found 5 papers, 2 papers with code
Towards a General GNN Framework for Combinatorial Optimization
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction.
Graph Positional and Structural Encoder
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering.
Taxonomy of Benchmarks in Graph Representation Learning
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry.
Towards a Taxonomy of Graph Learning Datasets
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.
Machine-Learning Driven Drug Repurposing for COVID-19
Our in-silico approach to drug repurposing has promise in identifying new drug candidates and treatments for other viruses.
