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 451–475 of 982 papers

TitleStatusHype
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Mitigating Relational Bias on Knowledge Graphs—0
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
MGTCOM: Community Detection in Multimodal GraphsCode0
Graph representation learning for street networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Implicit Graphon Neural RepresentationCode1
Application of Graph Neural Networks and graph descriptors for graph classification—0
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Show:102550
← PrevPage 19 of 40Next →

Benchmark Results

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