Today, social networks are becoming increasingly popular and indispensable, where users usually have multiple accounts. 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, conduct recommendations across social networks, and track cross social network user behaviors by user identity linkage. Existing works mainly focus on a specific type of user profile, user-generated content, and structural information. They have problems of weak data expression ability and ignored potential relationships, resulting in unsatisfactory performances of user identity linkage. Recently, graph neural networks have achieved excellent results in graph embedding, graph representation, and graph classification. As a graph has strong relationship expression ability, we propose a user identity linkage method based on a heterogeneous graph attention network mechanism (UIL-HGAN). Firstly, we represent user profiles, user-generated content, structural information, and their features in a heterogeneous graph. Secondly, we use multiple attention layers to aggregate user information. Finally, we use a multi-layer perceptron to predict user identity linkage. We conduct experiments on two real-world datasets: OSCHINA-Gitee and Facebook-Twitter. The results validate the effectiveness and advancement of UIL-HGAN by comparing different feature combinations and methods.

中文翻译：

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异构图注意力网络建模跨社交网络的用户身份链接

如今，社交网络变得越来越流行和必不可少，其中用户通常拥有多个帐户。跨社交网络进行用户身份链接具有相当重要的意义。我们可以全面描述用户行为的各种特征，准确地对用户配置文件进行建模，跨社交网络进行推荐，以及通过用户身份链接跟踪跨社交网络的用户行为。现有作品主要集中于特定类型的用户配置文件，用户生成的内容和结构信息。它们具有数据表达能力弱和潜在关系被忽略的问题，从而导致用户身份链接的性能不能令人满意。最近，图神经网络在图嵌入，图表示和图分类方面取得了出色的成果。由于图具有较强的关系表达能力，我们提出了一种基于异构图注意力网络机制（UIL-HGAN）的用户身份链接方法。首先，我们在异构图形中表示用户配置文件，用户生成的内容，结构信息及其功能。其次，我们使用多个关注层来聚合用户信息。最后，我们使用多层感知器来预测用户身份链接。我们在两个真实世界的数据集上进行实验：OSCHINA-Gitee和Facebook-Twitter。通过比较不同的特征组合和方法，结果验证了UIL-HGAN的有效性和先进性。用户生成的内容，结构信息及其在异构图中的特征。其次，我们使用多个关注层来聚合用户信息。最后，我们使用多层感知器来预测用户身份链接。我们在两个真实世界的数据集上进行实验：OSCHINA-Gitee和Facebook-Twitter。通过比较不同的特征组合和方法，结果验证了UIL-HGAN的有效性和先进性。用户生成的内容，结构信息及其在异构图中的特征。其次，我们使用多个关注层来聚合用户信息。最后，我们使用多层感知器来预测用户身份链接。我们在两个真实世界的数据集上进行实验：OSCHINA-Gitee和Facebook-Twitter。通过比较不同的特征组合和方法，结果验证了UIL-HGAN的有效性和先进性。