  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 focus on preserving 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 employ both structural proximity and attribute
proximity in the loss function to preserve the respective features and the
representations are obtained by minimizing the loss function. Results of
extensive experiments on four real-world datasets show that the proposed method
performs significantly better than baselines on a variety of tasks, which
demonstrate the effectiveness and generality of our method.
