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 281–290 of 982 papers

TitleStatusHype
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Differential Encoding for Improved Representation Learning over Graphs—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
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Benchmark Results

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