residual2vec with a stochastic gradient descent algorithm for embedding large networks #3
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Issue:
The current implementation based on a matrix factorization is memory demanding especially for large networks. The memory consumption is marginal up to 1M but considerable for larger networks, which prevents me to use residua2vec for some of my projects. This update aims to address this issue by using the stochastic gradient descent algorithm, which updates the embedding incrementally using a small chunk of data that can be fitted into memory.