# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
###### Abstract
If either of the two feature types is too faulty, it may lead to error propagation or additional noise. An example is shown in Figure 1. If the actually existing relations \(r_{6}\) and \(r_{2}^{\prime}\) are missing, user \(u_{2}\) can be easily aligned to \(u_{1}^{\prime}\) by mistake. Similarly, the linkage error might be propagated to the other pair \(u_{1}\) and \(u_{2}^{\prime}\) with the help of relations \(r_{1}\) and \(r_{1}^{\prime}\). Moreover, it is also possible for a careless algorithm to determine incorrectly that \(u_{5}\) and \(u_{5}^{\prime}\) are not the same natural person by comparing their names \"[PERSON] 001\" and \"[PERSON]\", or their affiliations \"AA Corp.\" and \"University of AAA\". Therefore, attributes and relations have different levels of importance in UA tasks when they are considered imbalanced. Distinguishing the contributions of attribute information and structural information benefits a great deal from enhancing the linking accuracy of UA models. This paper adopts an embedding-based approach to incorporate user attributes and network structures into a unified optimization framework, which ensures the robustness of the model. Supervised UA approaches that build learning models on a set of identified user pairs have brought convenience to train highly accurate models. The same technologies have also created some potential problems that are exposed in almost every supervised work. The alignment accuracy is limited by the quantity of pre-aligned user pairs (labeled data) across networks, while massive unidentified users (unlabeled data) are not utilized. In the meantime, pre-aligned user pairs are often insufficient due to costly manual annotations and the huge scale of social networks. **(Challenge 3) Making the most of unlabeled data for the alignment task remains a challenge that has not yet been fully resolved. ** A straightforward solution for exploiting unlabeled data is to predict new auto-labeled data iteratively based on initial training samples [15; 16]. To improve the quality of auto-labeled data, this work introduces quality filters based on the symmetry property of similarity function and adopts a threshold function to obtain higher alignment accuracy. To address the aforementioned three challenges in the UA problem, this paper proposes a novel embedding-based UA model, Joint embedding of Attributes and Relations for User Alignment (JARUA). The goal of JARUA is to map two social networks into a common latent space where aligned users are close to each other. In particular, all users' text attributes are first automatically divided into the subword level and the word level to unify the modeling of multi-type text attributes. Second, JARUA employs a graph attention network (GAT) [17] integrated with multi-granular attribute embedding to effectively leverage the attributes and network structure, based on which a unified objective function is designed for optimization. Third, an iterative training algorithm is developed to improve the model performance by making full use of unlabeled data. The algorithm trains the model on an initial set of labeled data and iteratively predicts new auto-labeled data, which are added to the training set for the next iteration. After each iteration, filtering measures are taken to ensure the quality of auto-labeled samples. The main contributions of this work include:  
* A novel embedding-based UA model to incorporate attributes and network structure into a unified optimization framework which is robust to user attributes missing or sparse networks. * A multi-granularity attribute representation method is developed to effectively process multi-type user attributes. * An iterative training algorithm with filtering mechanics, which utilizes numerous unlabeled data to bootstrap the model performances. * Extensive experiments on two real-world datasets demonstrate the superiority of JARUA over a series of comparison methods.