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 realworld datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.
Su et al. REF design a Constrained Dual Embedding model for multiple (more than two) social network, an effective NS-Alternation algorithm is adopted for the optimization.
MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation
IJCAI
Computer Science