RECT
Graph learner
A deep learning framework for graph representation learning with partially labeled data
This is the source code of "Network Embedding with Completely-Imbalanced Labels". TKDE2020
19 stars
5 watching
7 forks
Language: Python
last commit: over 4 years ago
Linked from 1 awesome list
graph-embeddinggraph-representation-learningnetwork-embedding
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | A sparsity-aware implementation of a deep learning algorithm for graph embedding with text information. | 74 |
| | A method for creating network embeddings when labeled data is imbalanced | 12 |
| | A framework for learning representations from attributed graphs, incorporating structural and attribute information. | 59 |
| | An implementation of a deep learning algorithm to generate node embeddings in graphs | 322 |
| | An implementation of a deep learning-based method for creating vector representations of nodes in a graph | 167 |
| | A software framework that integrates statistical relational learning and graph neural networks for semi-supervised object classification and unsupervised node representation learning. | 403 |
| | An implementation of a deep learning algorithm for graph data | 270 |
| | A PyTorch implementation of a semi-supervised graph classification model that learns hierarchical representations from labeled and unlabeled graph data. | 209 |
| | An implementation of a method for learning graph representations from global structural information in a network | 64 |
| | A reference implementation of graph embedding with clustering using deep learning techniques | 253 |
| | A software implementation of a graph embedding algorithm | 87 |
| | A Python framework for unsupervised learning on graph data using various network embedding and community detection techniques | 2,178 |
| | An implementation of learnable graph convolutional networks for efficient graph processing | 46 |
| | An implementation of an algorithm for learning graph representations from network data | 106 |
| | A software framework for building deep metric learning models using lifted structured feature embedding | 346 |