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 251–260 of 982 papers

TitleStatusHype
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Domain Adaptive Graph Classification—0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
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Benchmark Results

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