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 941–950 of 982 papers

TitleStatusHype
Graph Representation Learning: A SurveyCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Learning Semantic-Specific Graph Representation for Multi-Label Image RecognitionCode1
ChainNet: Learning on Blockchain Graphs with Topological Features—0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
Modeling Event Propagation via Graph Biased Temporal Point Process—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
DeepTrax: Embedding Graphs of Financial Transactions—0
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
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Benchmark Results

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