# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 1 Introduction  
With the rapid development of the Internet and information technology, social networks are becoming more and more indispensable and various. Due to different social networks embody different functions, the purpose and behavior characteristics of users are also different [1]. It is of considerable significance to conduct user identity linkage across social networks. We can comprehensively depict diversified characteristics of user behaviors, accurately model user profiles [2], conduct recommendations across social networks [3], and track user behaviors across social networks by user identity linkage. The existing methods of user identity linkage can be divided into three categories [4]: User profile-based, user generated content-based, and structural information-based. User profiles mainly include a username, nickname, avatar, etc. [PERSON] et al. [5] extracted features of patterns to human limitations, exogenous factors, and endogenous factors from username for user identity linkage. User-generated content includes time information, spatial information, and published content. [PERSON] et al. [6] extracted time information, spatial information, and others from user-generated content to analyze interest and writing style for user identity linkage. Structural information can be expressed as two forms: Directed link and undirected link. [PERSON] et al. [7] used the network embedding method to extract friend relationship information for user identity linkage. Some studies also use multiple kinds of data, such as [PERSON] et al. [8], extracted friend relationships and represented user interests in the latent space for user identity linkage. However, the existing user identity linkage methods have the following problems: (1) In some cross social network scenarios, user profiles, user-generated content, and structural information usually have problems of week unilateral expression ability, resulting in unsatisfactory user identity linkage performances. (2) There are potential relationships between user profiles, user-generated content, and structural information, which is often ignored when using a particular type of information alone. Therefore, we need an effective way to aggregate user profiles, user-generated content, and structural information to solve the above problems. Lately, graph neural networks (GNN) has achieved better performances on node representation learning [9]. GAT [10] uses node self-attention mechanisms to learn graph structure representation. GEMSEC [11] is a representation learning method that combines random walk, skip-gram, and clustering. GCN [12] is a semi-supervised graph structure representation method with graph convolutional networks. Most of the existing GNN methods are applied to the same data source for data mining, and are seldom applied to user identity linkage. As a graph has a strong relationship expression ability, we can express user profiles, user-generated content, and structural information in a heterogeneous graph. We can use GNN to learn feature representation and aggregate user profiles, user-generated content, and structural information in the latent space. In the paper, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). To solve problems of weak data expression ability and ignored potential relationships, firstly, user profiles, user-generated content, structural information, and their features are represented as nodes in a heterogeneous graph from different social networks, respectively. We build edges between these nodes and users. Secondly, we use multiple attention layers to represent users by aggregating user profiles, user-generated content, and structural information in the latent space. Finally, we use a multi-layer perceptron to predict user identity linkage from different social networks. The main contributions of this paper are summarized as follows:  
1.