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Network embedding is the process of learning low-dimensional representationsfor nodes in a network, while preserving node features. Existing studies onlyleverage network structure information and focus on preserving structuralfeatures. However, nodes in real-world networks often have a rich set ofattributes providing extra semantic information. It has been demonstrated thatboth structural and attribute features are important for network analysistasks. To preserve both features, we investigate the problem of integratingstructure and attribute information to perform network embedding and propose aMultimodal Deep Network Embedding (MDNE) method. MDNE captures the non-linearnetwork structures and the complex interactions among structures andattributes, using a deep model consisting of multiple layers of non-linearfunctions. Since structures and attributes are two different types ofinformation, a multimodal learning method is adopted to pre-process them andhelp the model to better capture the correlations between node structure andattribute information. We employ both structural proximity and attributeproximity in the loss function to preserve the respective features and therepresentations are obtained by minimizing the loss function. Results ofextensive experiments on four real-world datasets show that the proposed methodperforms significantly better than baselines on a variety of tasks, whichdemonstrate the effectiveness and generality of our method.

Multimodal Deep Network Embedding with Integrated Structure and Attribute Information