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 931–940 of 982 papers

TitleStatusHype
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
Fundamental Limits of Deep Graph Convolutional Networks—0
Graph Representation learning for Audio & Music genre Classification—0
Decoupling feature propagation from the design of graph auto-encoders—0
Relational Graph Representation Learning for Open-Domain Question Answering—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
On the Interpretability and Evaluation of Graph Representation Learning—0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Learning Robust Representations with Graph Denoising Policy Network—0
Universal Graph Transformer Self-Attention NetworksCode0
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Benchmark Results

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