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Analysis and visualization of an information network can be facilitatedbetter using an appropriate embedding of the network. Network embedding learnsa compact low-dimensional vector representation for each node of the network,and uses this lower dimensional representation for different network analysistasks. Only the structure of the network is considered by a majority of thecurrent embedding algorithms. However, some content is associated with eachnode, in most of the practical applications, which can help to understand theunderlying semantics of the network. It is not straightforward to integrate thecontent of each node in the current state-of-the-art network embedding methods.  In this paper, we propose a nonnegative matrix factorization basedoptimization framework, namely FSCNMF which considers both the networkstructure and the content of the nodes while learning a lower dimensionalrepresentation of each node in the network. Our approach systematicallyregularizes structure based on content and vice versa to exploit theconsistency between the structure and content to the best possible extent. Wefurther extend the basic FSCNMF to an advanced method, namely FSCNMF++ tocapture the higher order proximities in the network. We conduct experiments onreal world information networks for different types of machine learningapplications such as node clustering, visualization, and multi-classclassification. The results show that our method can represent the networksignificantly better than the state-of-the-art algorithms and improve theperformance across all the applications that we consider.

FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks