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 781–790 of 982 papers

TitleStatusHype
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Calibrating and Improving Graph Contrastive LearningCode0
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
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Benchmark Results

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