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 581–590 of 982 papers

TitleStatusHype
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning—0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
CN-Motifs Perceptive Graph Neural Networks—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
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Benchmark Results

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