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Directly publishing the original data of social networks may compromise personal privacy because social relationship data contain sensitive information about users. To protect the social relationships against inference attacks and achieve the trade-off between privacy and utility, we propose a privacy protection algorithm that combines the friendship links of central nodes (PPCN) in a dynamic social network. In the preparation work, we design two indices for user influence based on the characteristics of social networks that can identify central nodes (Definition 1) in a network. Operating central nodes can effectively protect user privacy and improve algorithm efficiency. Then we propose the PPCN algorithm to classify the friendship links of central nodes into three levels, which achieves the trade-off between privacy and utility. Considering that the added links may increase the risk of privacy disclosure, a substitution coefficient θ (Definition 4) is designed to measure the probability of two strangers becoming friends. Experimental results show that the privacy-utility trade-off (PUTO) value of the PPCN algorithm is an average 29.43% lower than that of other methods, achieving a better trade-off between privacy and structural utility. In addition, the PPCN algorithm only runs for 3.59 s, which performs better than most algorithms.

Abstract Hydrophilic polymer coatings on artificial implants generate excellent tribological properties. The friction properties of polymer coatings are affected by salt ion factors. Herein, the atomic force microscopy (AFM) was used to show that the superlubricity was achieved between poly(vinylphosphonic acid) (PVPA)-modified Ti6Al4V and polystyrene (PS) microsphere probe lubricated with monovalent salt solutions (LiCl, NaCl, KCl, and CsCl). Considering that adhesion is an important cause of friction changes, the AFM was further utilized to obtain adhesion between friction pairs in different salt solutions. The results indicated that the larger the cation radius in the lubricant, the smaller the adhesion, and the lower the friction coefficient of the PVPA coating. The electrostatic interaction between the PVPA and one-valence cations in lubricants was analyzed by the molecular dynamics (MD) simulation as it was found to be the main influencing factor of the adhesion. Combined analysis results of friction and adhesion indicated that by adjusting the size of cation radius in lubricant, the adhesion between the tribo-pairs can be changed, and eventually the magnitude of friction can be affected. This study opens up a new avenue for analyzing the friction characteristics of hydrophilic polymer coatings from the perspective of intermolecular forces.

With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users’ interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.