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 681–690 of 982 papers

TitleStatusHype
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
TopER: Topological Embeddings in Graph Representation Learning—0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation—0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Towards Fair Graph Representation Learning in Social Networks—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Towards Graph Representation Learning in Emergent Communication—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
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Benchmark Results

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