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 226–250 of 982 papers

TitleStatusHype
Fast Graph Representation Learning with PyTorch GeometricCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
How Powerful are Graph Neural Networks?Code1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning—0
Heterogeneous Temporal Hypergraph Neural Network—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Wasserstein Hypergraph Neural Network—0
Language Embedding Meets Dynamic Graph: A New Exploration for Neural Architecture Representation Learning—0
Graph Persistence goes Spectral—0
Positional Encoding meets Persistent Homology on GraphsCode0
Studying and Improving Graph Neural Network-based Motif Estimation—0
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation LearningCode0
Simple yet Effective Graph Distillation via Clustering—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Convexified Message-Passing Graph Neural Networks—0
Mini-Game Lifetime Value Prediction in WeChat—0
LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural NetworksCode0
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease DetectionCode0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
Show:102550
← PrevPage 10 of 40Next →

Benchmark Results

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