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 141–150 of 982 papers

TitleStatusHype
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
Scalable Graph Compressed ConvolutionsCode0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
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Benchmark Results

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