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 601–610 of 982 papers

TitleStatusHype
Pre-training Graph Neural Network for Cross Domain Recommendation—0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
Quantifying Challenges in the Application of Graph Representation Learning—0
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
Reconstruction for Powerful Graph Representations—0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Recursive Neighborhood Pooling for Graph Representation Learning—0
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Benchmark Results

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