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 726–750 of 982 papers

TitleStatusHype
Graph Representation Learning for Spatial Image Steganalysis—0
Graph representation learning for street networks—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Graph Representation Learning Towards Patents Network Analysis—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning with Diffusion Generative Models—0
Graph sampling for node embedding—0
Graph Self-Contrast Representation Learning—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Graph Transformers without Positional Encodings—0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
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Benchmark Results

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