Abstract
Recently, reconciling social networks receives significant attention. Most of the existing studies have
limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address
these three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In this framework, we first design a novel Constrained Dual Embedding model by simultaneously embedding and reconciling multiple social networks to formulate our problem into a unified optimization. To address this optimization, we then design an effective algorithm called NS-Alternating. We also prove that this algorithm converges to KKT points. Through extensive experiments on real-world datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.
Keywords
- Machine Learning: Data Mining
- Multidisciplinary Topics and Applications: Social Sciences
- Knowledge Representation and Reasoning: Information Fusion
- Multidisciplinary Topics and Applications: AI and the Web
