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 951–975 of 982 papers

TitleStatusHype
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
A Deep Latent Space Model for Graph Representation LearningCode0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision LevelsCode0
Topological Pooling on GraphsCode0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
Embedding Graphs on Grassmann ManifoldCode0
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation LearningCode0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Scalable and Efficient Temporal Graph Representation Learning via Forward Recent SamplingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Normed Spaces for Graph EmbeddingCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
Semi-Supervised Graph Attention Networks for Event Representation LearningCode0
OLGA: One-cLass Graph AutoencoderCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Towards Expressive Graph RepresentationCode0
Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain GraphCode0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
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Benchmark Results

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