littleballoffur
Graph sampler
A Python library for graph sampling methods to efficiently subsample complex networks
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
705 stars
10 watching
55 forks
Language: Python
last commit: almost 2 years ago
Linked from 5 awesome lists
community-structuredeep-learningforest-firegraphgraph-algorithmsgraph-embeddinggraph-samplinggraph-sparsificationmachine-learningmetropolis-hastingsminimum-spanning-treenetwork-analyticsnetwork-embeddingnetwork-samplingnetwork-sciencenetworkxnode-embeddingrandom-walksampling
josephmisiti/awesome-machine-learning
academic/awesome-datascience
oxnr/awesome-bigdata
briatte/awesome-network-analysis
krzjoa/awesome-python-data-scienceRelated projects:
| Repository | Description | Stars |
|---|---|---|
| benedekrozemberczki/karateclub | A Python framework for unsupervised learning on graph data using various network embedding and community detection techniques | 2,178 |
| benedekrozemberczki/walklets | An implementation of an algorithm for learning graph representations from network data | 106 |
| benedekrozemberczki/tene | A sparsity-aware implementation of a deep learning algorithm for graph embedding with text information. | 74 |
| benedekrozemberczki/attentionwalk | An implementation of a deep learning algorithm to generate node embeddings in graphs | 322 |
| benedekrozemberczki/asne | A software implementation of a method to reduce the dimensionality of attributed graphs | 83 |
| benedekrozemberczki/mixhop-and-n-gcn | A deep learning framework implementation of higher-order graph convolutional architectures and their applications | 403 |
| benedekrozemberczki/feather | A reference implementation of a method for learning graph node embeddings from graph structure and node features | 47 |
| benedekrozemberczki/sgcn | An implementation of a deep learning algorithm for graph data | 270 |
| benedekrozemberczki/seal-ci | A PyTorch implementation of a semi-supervised graph classification model that learns hierarchical representations from labeled and unlabeled graph data. | 209 |
| benedekrozemberczki/bane | A software implementation of a graph embedding algorithm | 87 |
| benedekrozemberczki/splitter | A PyTorch implementation of node representation learning using multiple social contexts | 213 |
| benedekrozemberczki/gemsec | A reference implementation of graph embedding with clustering using deep learning techniques | 253 |
| benedekrozemberczki/appnp | A PyTorch implementation of a graph neural network model that learns personalized node representations | 367 |
| benedekrozemberczki/gam | An implementation of a graph classification model using structural attention and PyTorch | 270 |
| benedekrozemberczki/clustergcn | A PyTorch implementation of a clustering algorithm for graph neural networks | 788 |