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 451–460 of 982 papers

TitleStatusHype
Graph Representation Learning for Contention and Interference Management in Wireless NetworksCode0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
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
Domain Adaptive Graph Classification—0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
LightGCN: Evaluated and EnhancedCode0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
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Benchmark Results

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