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 921–930 of 982 papers

TitleStatusHype
GraLSP: Graph Neural Networks with Local Structural Patterns—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
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
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Benchmark Results

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