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 7180 of 982 papers

TitleStatusHype
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Multi-hop Attention Graph Neural NetworkCode1
A step towards neural genome assemblyCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
A Representation Learning Framework for Property GraphsCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Show:102550
← PrevPage 8 of 99Next →

Benchmark Results

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