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

Showing 921930 of 982 papers

TitleStatusHype
Multi-Class and Multi-Task Strategies for Neural Directed Link PredictionCode0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Scalable Graph Compressed ConvolutionsCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Temporal knowledge graph representation learning with local and global evolutionsCode0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
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Benchmark Results

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