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

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Showing 551–575 of 982 papers

TitleStatusHype
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Connector 0.5: A unified framework for graph representation learningCode0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
What Do GNNs Actually Learn? Towards Understanding their RepresentationsCode0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Comprehensive Survey on Deep Graph Representation Learning—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Graph Representation Learning for Interactive Biomolecule Systems—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
FMGNN: Fused Manifold Graph Neural Network—0
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
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
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
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
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Benchmark Results

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