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 361370 of 982 papers

TitleStatusHype
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
LightGCN: Evaluated and EnhancedCode0
Heterogeneous Deep Graph InfomaxCode0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
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Benchmark Results

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