Transfer Learning across Networks for Collective Classification

This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201

Strongly Regular Graphs Constructed from pp-ary Bent Functions

In this paper, we generalize the construction of strongly regular graphs in [Y. Tan et al., Strongly regular graphs associated with ternary bent functions, J. Combin.Theory Ser. A (2010), 117, 668-682] from ternary bent functions to $p$-ary bent functions, where $p$ is an odd prime. We obtain strongly regular graphs with three types of parameters. Using certain non-quadratic $p$-ary bent functions, our constructions can give rise to new strongly regular graphs for small parameters.Comment: to appear in Journal of Algebraic Combinatoric

Study on the Continuance Usage of Mobile Health Management Application based on Uses and Gratifications Theory

In order to examine the influence effect of uses and gratifications of mobile health services. A research model was developed to study the influence of users’ utility gratification, hedonic gratification and social gratification from the perspective of Uses and Gratifications theory. SPSS and Smart PLS were employed to verify the research hypotheses using the empirical data collected via survey questionnaires. Research results show that utility gratification, hedonic gratification and social gratification all exert significant and positive effects on users’ continuance usage intention. Especially, gratification of health management, perceived fantasy, social image play important roles in users’ uses and gratifications. Providers of Mobile health management application should design and take personalized operating strategies and marketing strategies according to different needs