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Found 5 papers, 3 papers with code
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
Node Classification on Non-Homophilic (Heterophilic) Graphs
Node Property Prediction
Edge Proposal Sets for Link Prediction
Here, we demonstrate how simply adding a set of edges, which we call a \emph{proposal set}, to the graph as a pre-processing step can improve the performance of several link prediction algorithms.
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Link Property Prediction
on ogbl-ddi
Better Set Representations For Relational Reasoning
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope.
A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning
Word Sense Disambiguation is used to find meaning of words based on live contexts using supervised and unsupervised approaches.
A Hierarchical Network for Diverse Trajectory Proposals
In the indoor setting, we use an autonomous drone to navigate various scenarios and also a ground robot which can explore the environment using the trajectories proposed by our framework.
