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 611–620 of 982 papers

TitleStatusHype
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Graph representation learning for street networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Implications of sparsity and high triangle density for graph representation learning—0
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Benchmark Results

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