Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

• Main Authors: Jiangtao Ma, Yaqiong Qiao, Guangwu Hu, Yongzhong Huang, Meng Wang, Arun Kumar Sangaiah, Chaoqin Zhang, Yanjun Wang
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Anchor link inferring; graph partition; node matching; online social network; social network analysis
• Online Access: https://ieeexplore.ieee.org/document/7954610/

Along with the popularity of online social network (OSN), more and more OSN users tend to create their accounts in different OSN platforms. Under such circumstances, identifying the same user among different OSNs offers tremendous opportunities for many applications, such as user identification, migration patterns, influence estimation, and expert finding in social media. Different from existing solutions which employ user profile or social network structure alone, in this paper, we proposed a novel joint solution named MapMe, which takes both user profile and social network structure feature into account, so that it can adapt more OSNs with more accurate results. MapMe first calculates user similarity via profile features with the Doc2vec method. Then, it evaluates user similarity by analyzing user's ego network features. Finally, the profile features and ego network features were combined to measure the similarity of the users. Consequently, MapMe balances the two similarity factors to achieve goals in different platforms and scenarios. Finally, experiments are conducted on the synthetic and real data sets, proving that MapMe outperforms the existing methods with 10% on average.