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 891–900 of 982 papers

TitleStatusHype
K-Core based Temporal Graph Convolutional Network for Dynamic GraphsCode1
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
Π-nets: Deep Polynomial Neural NetworksCode1
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
Dual Graph Representation Learning—0
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Benchmark Results

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