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 401–425 of 982 papers

TitleStatusHype
A Survey on Malware Detection with Graph Representation Learning—0
Topological Pooling on GraphsCode0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail—0
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations—0
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Learnable Topological Features for Phylogenetic Inference via Graph Neural NetworksCode1
Creating generalizable downstream graph models with random projections—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Is Distance Matrix Enough for Geometric Deep Learning?Code1
A Survey on Spectral Graph Neural Networks—0
Heterophily-Aware Graph Attention Network—0
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal KrigingCode1
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Benchmark Results

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