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 371–380 of 982 papers

TitleStatusHype
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Graph Propagation Transformer for Graph Representation LearningCode1
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
Neural Oscillators are Universal—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
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Benchmark Results

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