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

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Showing 301–325 of 982 papers

TitleStatusHype
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Deep Learning on Graphs for Natural Language Processing—0
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail—0
Deep Graph Generators: A Survey—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
Deep Feature Learning for Graphs—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Graph Learning with Localized Neighborhood Fairness—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
Decoupling feature propagation from the design of graph auto-encoders—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
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Benchmark Results

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