# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 2 Related Work  
UA was initially formalized as connecting corresponding user identities across different communities in [18], which is also called user identity linkage (UIL), user identification, anchor link prediction, etc. Designing UA models aims to align the social accounts belonging to identical natural person among multiple social media platforms. These pairs  
Figure 1: An example of user alignment across social networks. \(G\) and \(G^{\prime}\) denote two networks to be aligned. The lines between users within each network represent the relations between users, where solid lines (\(r_{1}\) and \(r_{1}^{\prime}\), etc.) and dashed lines (\(r_{6}\) and \(r_{2}^{\prime}\)) represent actual existing user relations and potential but not observed relations, respectively. The lines between two users across \(G\) and \(G^{\prime}\) denote the UA problem. The solutions determine whether potential user relations actually exist or not. of identical users who act as anchors connecting various networks are known as aligned user pairs. This section attempts to provide a brief summary of the literature relating to UA issues. Existing UA methods can be roughly classified into three categories: supervised, semi-supervised, and unsupervised approaches. Most research is supervised, where the main idea is training a ranking model or a binary classifier to find potential identical users by using pre-matched user pairs as guidance [3; 6; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. For example, [PERSON] et al. [22] proposed an anchor link prediction method PALE for preserving significant structural regularities of networks with an awareness of supervised pre-aligned users. Likewise, [PERSON] et al. [23] provided an approach ULink, which attempts to map the users from multiple social networks into the same latent user space through a projection matrix. [PERSON] et al. [6] employed a graph neural network (GNN) to directly incorporate target users' ego networks into an embedding space by presenting a model MEGo2 vec. [PERSON] et al. [25] represented the social structure by determining the weighted contribution probabilities of friendships based on an attention-based network embedding (ABNE). [PERSON] et al. [26] introduced the UA model SAUIL, a Siamese neural network, to learn the high-level representation of users' web-browsing behaviors. [PERSON] et al. [27] extracted the structural information redundancies by analyzing the similarities of \(k\)-hop neighbors and integrated them with name-based attribute features for user identification. [PERSON] et al. [28] exploited the higher-order structural properties and alignment-oriented structural consistency to learn a unified graph embedding method (MGGE), which aimed to learn feature vectors of the graph. A recent study by [PERSON] et al. [29] considered UA as a sequential decision problem and proposed a reinforcement learning model to align users from a global perspective. The above published supervised studies on UA issues depend heavily on the quantity and quality of labeled data. However, collecting sufficient aligned user pairs as annotations is inevitable. Some unsupervised methods are capable of addressing the UA problem without any labeled data [13; 15; 30; 31; 32; 33; 34; 35; 36]. [PERSON] et al. [30] proposed an improved \(n\)-gram model that can automatically generate training data according to an evaluation of the username rareness. Then, a binary classifier was trained to recognize identical users. [PERSON] et al. [32] highlighted the function of heuristic string similarities to align user attributes through simple greedy matching (SiGMa). Some researchers have focused on feature mapping or distance optimization. [PERSON] et al. [33] suggested an approach of dynamic core interests (DCIM) that integrates network structure and user-generated contents to align users. [PERSON] et al. [31] recommended the model UUIL that can utilize earth mover's distance to measure the distance between user degree distributions.