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 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.Comment: 15 pages, 10 figure

MAUIL: Multi-level Attribute Embedding for Semi-supervised User Identity Linkage

User Identity Linkage (UIL) across social networks has recently attracted an increasing amount of attention for its significant research challenges and practical value. Most of the existing methods use a single way to express different types of attribute features. However, the simplex pattern can neither cover the entire set of different attribute features, nor capture higher-level semantic features in attribute text. This paper established a novel semi-supervised model, namely MAUIL, to seek the collective user identity between two arbitrary social networks. MAUIL includes two components: multi-level attribute embedding and Regularized Canonical Correlation Analysis (RCCA) based linear projection. Specifically, the text attributes for each network are divided into three types: character-level, word-level, and topic-level attributes. First, unsupervised approaches are employed to generate corresponding three types of text attribute vectors. Second, incorporating user relationship features to attribute features contributes a lot to enhance user representations. As a result, the final multi-level representation of the two networks can be obtained by combining the four type feature vectors. On the other hand, this work introduces RCCA to construct mappings from social networks to feature spaces. The mappings can project the social networks into a common correlated space for user identity linkage. We demonstrate the superiority of the proposed method over the state-of-the-art ones through extensive experiments on two real-world data sets. All the data sets and codes are publicly available online.Comment: 16 pages, 9 figure