GitHub - xiaohan2012/snpp: signed network partition and prediction

pre-processing

Make the graph into sparse matrix and save to data/{dataset}.npz
Use snpp.utils.data.load_train_test_graphs to split/load rain/test data
For Gephi format, run python snpp/utils/gephi.py (remeber to change the dataset in the script)

Name		Name	Last commit message	Last commit date
Latest commit History 63 Commits
data		data
output		output
snpp		snpp
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
correlation_clustering_example.ipynb		correlation_clustering_example.ipynb
experiment.ipynb		experiment.ipynb
experiment.py		experiment.py
grid_search_on_lowrank.ipynb		grid_search_on_lowrank.ipynb
iterative_vs_lowrank_on_confident_edges.ipynb		iterative_vs_lowrank_on_confident_edges.ipynb
iterative_vs_single.ipynb		iterative_vs_single.ipynb
lowrank_experiment.ipynb		lowrank_experiment.ipynb
lowrank_test.output		lowrank_test.output
motivation_demo.py		motivation_demo.py
n_order_triangles.ipynb		n_order_triangles.ipynb
n_order_triangles.py		n_order_triangles.py
predict_by_undirectionality.ipynb		predict_by_undirectionality.ipynb
process_dataset.py		process_dataset.py
process_wiki_rfa.py		process_wiki_rfa.py
profile_lowrank_partitioning.py		profile_lowrank_partitioning.py
profile_max_balance_greedy.py		profile_max_balance_greedy.py
rank_parameter_effect.ipynb		rank_parameter_effect.ipynb
requirements.txt		requirements.txt
spectral_embedding.ipynb		spectral_embedding.ipynb
strong_vs_weak_balance.ipynb		strong_vs_weak_balance.ipynb
triangle_type_frequency.ipynb		triangle_type_frequency.ipynb