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 601–625 of 982 papers

TitleStatusHype
Robust Graph Representation Learning for Local Corruption RecoveryCode0
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Structure-Aware Transformer for Graph Representation LearningCode2
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Urban Region Profiling via A Multi-Graph Representation Learning Framework—0
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft—0
Molecular Representation Learning via Heterogeneous Motif Graph Neural NetworksCode1
When Do Flat Minima Optimizers Work?Code1
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
GRPE: Relative Positional Encoding for Graph TransformerCode1
Graph Representation Learning via Aggregation EnhancementCode1
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Neural Approximation of Graph Topological FeaturesCode1
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
How Expressive are Transformers in Spectral Domain for Graphs?Code1
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Identifying critical nodes in complex networks by graph representation learning—0
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
Dual Space Graph Contrastive Learning—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
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Benchmark Results

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