Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

Network embedding is the process of learning low-dimensional representations for nodes in a network while preserving node features. Existing studies only leverage network structure information and emphasize the preservation of structural features. However, nodes in real-world networks often have a rich set of attributes providing extra semantic information. It has been demonstrated that both structural and attribute features are important for network analysis tasks. To preserve both features, we investigate the problem of integrating structure and attribute information to perform network embedding and propose a multimodal deep network embedding (MDNE) method. MDNE captures the non-linear network structures and the complex interactions among structures and attributes using a deep model consisting of multiple layers of non-linear functions. Since structures and attributes are two different types of information, a multimodal learning method is adopted to pre-process them and help the model to better capture the correlations between node structure and attribute information. We define the loss function employing structural and attribute proximities to preserve the respective features, and the representations are obtained by minimizing the loss function. Results of extensive experiments on four real-world data sets show that the proposed method performs significantly better than baselines on a variety of tasks, which demonstrates the effectiveness and generality of our method.