SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 761–770 of 982 papers

TitleStatusHype
Hierarchical Transformer for Scalable Graph Learning—0
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
Hop-Hop Relation-aware Graph Neural Networks—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Identifying critical nodes in complex networks by graph representation learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified