Learning to Map Social Network Users by Unified Manifold Alignment on Hypergraph

Nowadays, a lot of people possess accounts on multiple online social networks, e.g., Facebook and Twitter. These networks are overlapped, but the correspondences between their users are not explicitly given. Mapping common users across these social networks will be beneficial for applications such a...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-12, Vol.29 (12), p.5834-5846
Main Authors: Zhao, Wei, Tan, Shulong, Guan, Ziyu, Zhang, Boxuan, Gong, Maoguo, Cao, Zhengwen, Wang, Quan
Format: Article
Language:English
Subjects:
Alignment
Calibration
Facebook
Graph theory
Graphs
Hypergraph
Learning systems
manifold alignment
Manifolds
Mapping
Social discrimination learning
Social interactions
Social networks
Social organization
Social structure
Task analysis
Training
Twitter
user mapping
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Summary:Nowadays, a lot of people possess accounts on multiple online social networks, e.g., Facebook and Twitter. These networks are overlapped, but the correspondences between their users are not explicitly given. Mapping common users across these social networks will be beneficial for applications such as cross-network recommendation. In recent years, a lot of mapping algorithms have been proposed which exploited social and/or profile relations between users from different networks. However, there is still a lack of unified mapping framework which can well exploit high-order relational information in both social structures and profiles. In this paper, we propose a unified hypergraph learning framework named unified manifold alignment on hypergraph (UMAH) for this task. UMAH models social structures and user profile relations in a unified hypergraph where the relative weights of profile hyperedges are determined automatically. Given a set of training user correspondences, a common subspace is learned by preserving the hypergraph structure as well as the correspondence relations of labeled users. UMAH intrinsically performs semisupervised manifold alignment with profile information for calibration. For a target user in one network, UMAH ranks all the users in the other network by their probabilities of being the corresponding user (measured by similarity in the subspace). In experiments, we evaluate UMAH on three real world data sets and compare it to state-of-art baseline methods. Experimental results have demonstrated the effectiveness of UMAH in mapping users across networks.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2018.2812888