# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
###### Abstract  
User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structure on UA. However, there have been few in-depth studies into the existing challenges for the joint integration of different types of text attributes, the imbalance between user attributes and network structure, and the utilization of massive unidentified users. To this end, this paper presents a high-accuracy embedding model named Joint embedding of Attributes and Relations for User Alignment (JARUA), to tackle the UA problem. First, a mechanism that can automatically identify the granularity of user attributes is introduced for handling multi-type user attributes. Second, a graph attention network is employed to extract the structural features and is integrated with user attributes features. Finally, an iterative training algorithm with quality filters is introduced to bootstrap the model performances. We evaluate JARUA on two real-world data sets. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art approaches. user alignment; social networks; multi-granularity; iterative training +
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number of RL techniques including autoencoders (AE) [7], convolution neural networks (CNN) [6] and recurrent neural networks (RNN) [12] have been adopted for extracting text features. However, such methods generally take a single method to encode the attribute text (e.g., one-hot encoding, bag-of-word vectors, and TF-IDF), which ignores the characteristics of different types of text. A perfect example can be observed in the following four types of attributes. Usermaes often have a large proportion of customized words with special symbols, e.g., \"S.Joshi\" and \"\(\clubsuit\)Joshi\(\clubsuit\)\". Age is generally represented by an integer, e.g., 18 and 60. Affiliation is usually a phrase that consists of some regular words, e.g., the full name of a corporation or a university. Blog/post may include many paragraphs rich with spelling mistakes, slang, or domain-specific jargon. **(Challenge 1) There still exists a challenge in jointly integrating different types of text attributes. ** Generally, text data, regardless of its length, contain a coarse- to fine-grained hierarchical structure. For example, letters can be combined together to form words, and phrases can be decomposed into words. In other words, granular structure naturally exists in texts. If we can find a proper representation method that conforms to the inner structure of the text attribute, it would be more efficient to capture the text features. This work attempts to handle multi-type text attributes in a multi-granularity approach which automatically discovers the inner granularity structure of attribute texts. Another line of research for UA utilizes the structural information of social networks. More current methods incorporate both user attributes and network structures to enhance the UA performance [13; 14]. For instance, the model REGAL [13] captures the node's structural identity and attribute identity by a matrix factorization-based approach. Another typically related study on UA feature combination is undertaken in CoLink [15], which iteratively learns an attribute-based and a structure-based model with a co-training strategy. However, **(Challenge 2) it is not a trivial task to effectively leverage user attributes and network structures to address UA problems when they are quite imbalanced. ** The attributes and relationships of users in real social networks can be diverse. Some users offer little profiles on their home pages but have rich connections with others. On the contrary, a number of users on LinkedIn give detailed personal information but they do not like to interact with others. This imbalance results in the low accuracy of user identification algorithms.