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 831–840 of 982 papers

TitleStatusHype
GripNet: Graph Information Propagation on Supergraph for Heterogeneous GraphsCode1
Geometric Scattering Attention NetworksCode0
Graph Contrastive Learning with Adaptive AugmentationCode1
Personalised Meta-path Generation for Heterogeneous GNNsCode1
XLVIN: eXecuted Latent Value Iteration Nets—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Bi-GCN: Binary Graph Convolutional NetworkCode1
Towards Expressive Graph RepresentationCode0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Reward Propagation Using Graph Convolutional NetworksCode1
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Benchmark Results

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