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 426–450 of 982 papers

TitleStatusHype
Spectral Augmentations for Graph Contrastive Learning—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Simultaneous Linear Multi-view Attributed Graph Representation Learning and ClusteringCode1
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption—0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Logical Message Passing Networks with One-hop Inference on Atomic FormulasCode1
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Everything is Connected: Graph Neural Networks—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning—0
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation LearningCode0
A Generalization of ViT/MLP-Mixer to GraphsCode1
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
Graph Learning with Localized Neighborhood Fairness—0
Data Augmentation on Graphs: A Technical SurveyCode1
Robust Graph Representation Learning via Predictive Coding—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Learning Graph Search Heuristics—0
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Benchmark Results

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