Recently, reconciling social networks, identifying the accounts belonging to the same individual across social networks, receives significant attention from both academic and industry. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these limitations, we rethink this problem and, for the first time, robustly and comprehensively reconcile multiple social networks. In this paper, we propose two frameworks, MASTER and MASTER+, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In MASTER, we first design a novel Constrained Dual Embedding model, simultaneously embedding and reconciling multiple social networks, to formulate this problem into a unified optimization. To address this optimization, we then design an effective NS-Alternating algorithm and prove it converges to KKT points. To further speed up MASTER, we propose a scalable framework, namely MASTER+. The core idea is to group accounts into clusters and then perform MASTER in each cluster in parallel. Specifically, we design an efficient Augmented Pre-Embedding model and Balance-aware Fuzzy Clustering algorithm for the high efficiency and the high accuracy. Extensive experiments demonstrate that both MASTER and MASTER+ outperform the state-of-the-art approaches. Moreover, MASTER+ inherits the effectiveness of MASTER and enjoys higher efficiency.

中文翻译：

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有效且高效地协调多个社交网络：一种嵌入方法

最近，协调社交网络，识别跨社交网络属于同一个人的帐户，受到学术界和工业界的极大关注。现有的研究大多在以下三个方面存在局限性：多样性、综合性和稳健性。为了解决这些限制，我们重新思考了这个问题，并首次稳健而全面地协调了多个社交网络。在本文中，我们提出了两个框架，MASTER 和 MASTER+，即跨多个社交网络，集成属性和结构嵌入以进行协调。在 MASTER 中，我们首先设计了一个新颖的 Constrained Dual Embedding 模型，同时嵌入和协调多个社交网络，将这个问题表述为一个统一的优化。为了解决这个优化问题，然后我们设计了一个有效的 NS-Alternating 算法并证明它收敛到 KKT 点。为了进一步加速 MASTER，我们提出了一个可扩展的框架，即 MASTER+。核心思想是将账户分组到集群中，然后在每个集群中并行执行MASTER。具体来说，我们设计了一种高效的增强预嵌入模型和平衡感知模糊聚类算法，以实现高效率和高精度。大量实验表明 MASTER 和 MASTER+ 都优于最先进的方法。而且，MASTER+ 继承了 MASTER 的有效性，并享有更高的效率。核心思想是将账户分组到集群中，然后在每个集群中并行执行MASTER。具体来说，我们设计了一种高效的增强预嵌入模型和平衡感知模糊聚类算法，以实现高效率和高精度。大量实验表明 MASTER 和 MASTER+ 都优于最先进的方法。而且，MASTER+ 继承了 MASTER 的有效性，并享有更高的效率。核心思想是将账户分组到集群中，然后在每个集群中并行执行MASTER。具体来说，我们设计了一种高效的增强预嵌入模型和平衡感知模糊聚类算法，以实现高效率和高精度。大量实验表明 MASTER 和 MASTER+ 都优于最先进的方法。而且，MASTER+ 继承了 MASTER 的有效性，并享有更高的效率。