CAMU: Cycle-Consistent Adversarial Mapping Model for User Alignment Across Social Networks

The user alignment problem that establishes a correspondence between users across networks is a fundamental issue in various social network analyses and applications. Since symbolic representations of users suffer from sparsity and noise when computing their cross-network similarities, the state-of-the-art methods embed users into the low-dimensional representation space, where their features are preserved and establish user correspondence based on the similarities of their low-dimensional embeddings. Many embedding-based methods try to align latent spaces of two networks by learning a mapping function before computing similarities. However, most of them learn the mapping function largely based on the limited labeled aligned user pairs and ignore the distribution discrepancy of user representations from different networks, which may lead to the overfitting problem and affect the performance. To address the above problems, we propose a cycle-consistent adversarial mapping model to establish user correspondence across social networks. The model learns mapping functions across the latent representation spaces, and the representation distribution discrepancy is addressed through the adversarial training between the mapping functions and the discriminators as well as the cycle-consistency training. Besides, the proposed model utilizes both labeled and unlabeled users in the training process, which may alleviate the overfitting problem and reduce the number of labeled users required. Results of extensive experiments demonstrate the effectiveness of the proposed model on user alignment on real social networks.