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FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network.
Social and Information Networks
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
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