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 951–982 of 982 papers

TitleStatusHype
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach—0
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling—0
Graph Convolutional Networks with EigenPoolingCode0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
Fast Graph Representation Learning with PyTorch GeometricCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
Representation Learning for Spatial Graphs—0
Adversarial Classifier for Imbalanced Problems—0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Discriminative Graph Autoencoder—0
SGR: Self-Supervised Spectral Graph Representation Learning—0
Multi-Task Graph AutoencodersCode0
How Powerful are Graph Neural Networks?Code1
Adaptive Sampling Towards Fast Graph Representation LearningCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
Open Domain Question Answering Using Early Fusion of Knowledge Bases and TextCode0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage—0
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
Hyperbolic Neural NetworksCode0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Learning to Make Predictions on Graphs with AutoencodersCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Marginalized graph autoencoder for graph clustering—0
Deep Feature Learning for Graphs—0
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Benchmark Results

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