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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 161–170 of 982 papers

TitleStatusHype
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Edge Representation Learning with HypergraphsCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
Show:102550
← PrevPage 17 of 99Next →

Benchmark Results

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