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 701–725 of 982 papers

TitleStatusHype
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
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
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
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
Dual Space Graph Contrastive Learning—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
Local2Global: A distributed approach for scaling representation learning on graphsCode0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Semi-Supervised Graph Attention Networks for Event Representation LearningCode0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
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Benchmark Results

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