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 951–960 of 982 papers

TitleStatusHype
Modeling Event Propagation via Graph Biased Temporal Point Process—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
DeepTrax: Embedding Graphs of Financial Transactions—0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Graph Convolutional Networks with EigenPoolingCode0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
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Benchmark Results

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