Network representation learning with rich text information

Representation learning has shown its effectiveness in many tasks such as image classification and tex-t mining. Network representation learning aims at learning distributed vector representation for each vertex in a network, which is also increasingly rec-ognized as an important aspect for network anal-ysis. Most network representation learning meth-ods investigate network structures for learning. In reality, network vertices contain rich information (such as text), which cannot be well applied with algorithmic frameworks of typical representation learning methods. By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), we propose…