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 121–130 of 982 papers

TitleStatusHype
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Multi-object event graph representation learning for Video Question Answering—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation LearningCode1
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
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Benchmark Results

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