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DeepWalk

swMATH ID: 39604
Software Authors: Bryan Perozzi, Rami Al-Rfou, Steven Skiena
Description: DeepWalk: Online Learning of Social Representations. We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk’s latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk’s representations can provide F1 scores up to 10
Homepage: https://paperswithcode.com/paper/deepwalk-online-learning-of-social
Source Code:  https://github.com/phanein/deepwalk
Related Software: node2vec; word2vec; GraRep; t-SNE; struc2vec; Adam; metapath2vec; PyTorch; Scikit; GitHub; ImageNet; DynGEM; AlexNet; ArnetMiner; LIBLINEAR; graph2vec; PTE; darch; FastGCN; RDF2vec
Cited in: 79 Publications
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Cited by 302 Authors

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2 Xia, Feng
2 Zhang, Da
2 Zhang, Kunpeng
2 Zhu, Xingquan
1 Ahmed, Nesreen K.
1 Amancio, Diego R.
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1 Böhm, Christian
1 Bourqui, Romain
1 Brajović, Miloš
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1 Chen, C. L. Philip
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1 Constantinides, Anthony G.
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1 Fan, Ying
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