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ShareScore release 0.7.1
Dataset results
7 results for “node2vec”
Node2Vec model - Czech Wikidata (knowledge graph / concepts / l80 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) concepts using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 80</li> <li>number of random walks = 40</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / concepts / l40 / rw10)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) concepts using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 40</li> <li>number of random walks = 10</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / labels / l80 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) labels using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 80</li> <li>number of random walks = 40</li> </ul>
Node2Vec model - Czech Wikidata (knowledge graph / labels / l160 / rw40)
<p>Node2Vec embedding model trained on Czech wikidata (from October 2020) labels using gensim implementation of Word2Vec with the following parameters for random walks:</p> <ul> <li>length of walk = 160</li> <li>number of random walks = 40</li> </ul>
Accurately modeling biased random walks on weighted networks using node2vec+ - Additional data
<p>Human gene interaction network data used to reproduce gene classification experiments https://github.com/krishnanlab/node2vecplus_benchmarks</p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20</p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), 80/20 training/test split, produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: </p> <p>These embeddings and weights were produced from this notebook on or around Sep 8, 2020:</p> <p>https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph%20embedding%20using%20SkipGram%20homogeneous%20graph.ipynb</p> <p>SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy<br> SkipGram_80_20_training_test_epoch_500_delta_0.0001_weights.h5</p> <p>I'm also including two runs just before this, with different epoch number and delta values:</p> <p>SkipGram_80_20_training_test_embedding_sep_6_2020_epoch_200_delta_0.001.npy</p> <p>SkipGram_80_20_training_test_weights_sep_6_2020_epoch_200_delta_0.001.h5</p> <p>SkipGram_80_20_training_test_embedding_sep_7_2020_epoch_200_delta_0.0001.npy<br> SkipGram_80_20_training_test_weights_sep_7_2020_epoch_200_delta_0.0001.h5</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.