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 451–500 of 982 papers

TitleStatusHype
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
Domain Adaptive Graph Classification—0
LightGCN: Evaluated and EnhancedCode0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
Dynamic Spiking Framework for Graph Neural Networks—0
Symmetry Breaking and Equivariant Neural Networks—0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
Understanding Community Bias Amplification in Graph Representation Learning—0
On the Initialization of Graph Neural NetworksCode0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
Normed Spaces for Graph EmbeddingCode0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Graph Representation Learning for Infrared and Visible Image Fusion—0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Graph AI in Medicine—0
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural NetworksCode0
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
Transformers are efficient hierarchical chemical graph learnersCode0
Learning node representation via Motif CoarseningCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
Graph Representation Learning Towards Patents Network Analysis—0
Deep Prompt Tuning for Graph Transformers—0
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Benchmark Results

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