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 276–300 of 982 papers

TitleStatusHype
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical UnderstandingCode1
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
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
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
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Graph Representation Learning for Infrared and Visible Image Fusion—0
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
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation—0
UniMAP: Universal SMILES-Graph Representation LearningCode1
Graph AI in Medicine—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation LearningCode0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
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Benchmark Results

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