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Description
🚀 The feature, motivation and pitch
I would like to implement the paper Attributed Network Embedding via Subspace Discovery by Zhang et al.
This paper proposes a unified framework for attributed network embedding, Attri2vec, that learns node-embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. Unlike other structure-aware node-embedding methods, this approach does not rely on message passing mechanism and hence very scalable.
Alternatives
Attri2vec is currently implemented in Stellargraph, which is based on Tensorflow.
Additional context
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