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  • Open Access

    ARTICLE

    Online Group Recommendation with Local Optimization

    Haitao Zou*, 1, Yifan He1, Shang Zheng1, Hualong Yu1, Chunlong Hu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.115, No.2, pp. 217-231, 2018, DOI: 10.3970/cmes.2018.00194

    Abstract There are some scenarios that need group recommendation such as watching a movie or a TV series, selecting a tourist destination, or having dinner together. Approaches in this domain can be divided into two categories: Creating group profiles and aggregating individual recommender list. Yet none of the above methods can handle the online group recommendation both efficiently and accurately and these methods either strongly limited by their application environment, or bring bias towards those users having limited connections with this group. In this work, we propose a local optimization framework, using sub-group profiles to compute the item relevance. Such method… More >

  • Open Access

    ARTICLE

    Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

    Pingshui Wang1,*, Zecheng Wang1, Qinjuan Ma1

    Journal of Information Hiding and Privacy Protection, Vol.1, No.1, pp. 35-42, 2019, DOI:10.32604/jihpp.2019.05942

    Abstract The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control. There is little research on the association of user privacy information, so it is not easy to design personalized privacy protection strategy, but also increase the complexity of user privacy settings. Therefore, this paper concentrates on the association of user privacy information taking big data analysis tools, so as to provide data support for personalized privacy protection strategy design. More >

  • Open Access

    ARTICLE

    An Influence Maximization Algorithm Based on the Influence Propagation Range of Nodes

    Yong Hua1,Bolun Chen1,2,∗,Yan Yuan1, Guochang Zhu1, Fenfen Li1

    Journal on Internet of Things, Vol.1, No.2, pp. 77-88, 2019, DOI:10.32604/jiot.2019.05941

    Abstract The problem of influence maximization in the social network G is to find k seed nodes with the maximum influence. The seed set S has a wider range of influence in the social network G than other same-size node sets. The influence of a node is usually established by using the IC model (Independent Cascade model) with a considerable amount of Monte Carlo simulations used to approximate the influence of the node. In addition, an approximate effect (1-1/e) is obtained, when the number of Monte Carlo simulations is 10000 and the probability of propagation is very small. In this paper,… More >

  • Open Access

    ARTICLE

    Personalized Privacy Protecting Model in Mobile Social Network

    Pingshui Wang1,*, Zecheng Wang1, Tao Chen1,2, Qinjuan Ma1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 533-546, 2019, DOI:10.32604/cmc.2019.05570

    Abstract With the rapid development of the new generation of information technology, the analysis of mobile social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. In this paper, we summarize the main access control model in mobile social network, analyze their contribution and point out their disadvantages. On this basis, a practical privacy policy is defined through authorization model supporting personalized privacy preferences. Experiments have been conducted on synthetic data sets. The result shows that the proposed privacy protecting model could improve the security of… More >

  • Open Access

    ARTICLE

    An Influence Maximization Algorithm Based on the Mixed Importance of Nodes

    Yong Hua1, Bolun Chen1,2,*, Yan Yuan1, Guochang Zhu1, Jialin Ma1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 517-531, 2019, DOI:10.32604/cmc.2019.05278

    Abstract The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network. Therefore, the comprehensive influence of node needs to be considered, when we choose the most influential node set consisted of k seed nodes. On account of the traditional methods used to measure the influence of nodes, such as degree centrality, betweenness centrality and closeness centrality, consider only a single aspect of the influence of node, so the influence measured by traditional methods mentioned above of node is not accurate. In this paper, we obtain the following result through experimental… More >

  • Open Access

    ARTICLE

    Modeling and Predicting of News Popularity in Social Media Sources

    Kemal Akyol1,*, Baha Şen2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

    Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More >

  • Open Access

    ARTICLE

    Examining the Impacts of Key Influencers on Community Development

    Di Shang1,*, Mohammed Ghriga1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 1-10, 2019, DOI:10.32604/cmc.2019.08217

    Abstract In this research, we aim to identify and investigate the impacts of key influencers on community formations and developments. We assess the impacts of key influencers by analyzing the activities and structure of the social media presence of a local community. Results of our analysis show that key influencers play important roles in connecting the community, transferring information, and improving overall sentiment of the community members. Our findings suggest that community practitioners can apply social network analysis to identify value-added influencers and discover strategies for improving the community and keeping leadership roles. More >

  • Open Access

    ARTICLE

    Analyzing Dynamic Change in Social Network Based on Distribution-Free Multivariate Process Control Method

    Yan Liu1,*, Lian Liu1, Yu Yan2, Hao Feng1, Shichang Ding3

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1123-1139, 2019, DOI:10.32604/cmc.2019.05619

    Abstract Social organizations can be represented by social network because it can mathematically quantify and represent complex interrelated organizational behavior. Exploring the change in dynamic social network is essential for the situation awareness of the corresponding social organization. Social network usually evolves gradually and slightly, which is hard to be noticed. The statistical process control techniques in industry field have been used to distinguish the statistically significant change of social network. But the original method is narrowed due to some limitation on measures. This paper presents a generic framework to address the change detection problem in dynamic social network and introduces… More >

  • Open Access

    ARTICLE

    Research on Public Opinion Propagation Model in Social Network Based on Blockchain

    Gengxin Sun1,*, Sheng Bin1, Meng Jiang2, Ning Cao3, Zhiyong Zheng4, Hongyan Zhao5, Dongbo Wang6, Lina Xu7

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1015-1027, 2019, DOI:10.32604/cmc.2019.05644

    Abstract With the emergence and development of blockchain technology, a new type of social networks based on blockchain had emerged. In these social networks high quality content creators, filters and propagators can all be reasonably motivated. Due to the transparency and traceability brought by blockchain technology, the public opinion propagation in such social networks presents new characteristics and laws. Based on the theory of network propagation and blockchain, a new public opinion propagation model for this kind of social network based on blockchain technology is proposed in this paper. The model considers the effect of incentive mechanism produced by reasonably quantifying… More >

  • Open Access

    ARTICLE

    Collaborative Filtering Recommendation Algorithm Based on Multi-Relationship Social Network

    Sheng Bin1,*, Gengxin Sun1, Ning Cao2, Jinming Qiu2, Zhiyong Zheng3, Guohua Yang4, Hongyan Zhao5, Meng Jiang6, Lina Xu7

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 659-674, 2019, DOI:10.32604/cmc.2019.05858

    Abstract Recommendation system is one of the most common applications in the field of big data. The traditional collaborative filtering recommendation algorithm is directly based on user-item rating matrix. However, when there are huge amounts of user and commodities data, the efficiency of the algorithm will be significantly reduced. Aiming at the problem, a collaborative filtering recommendation algorithm based on multi-relational social networks is proposed. The algorithm divides the multi-relational social networks based on the multi-subnet complex network model into communities by using information dissemination method, which divides the users with similar degree into a community. Then the user-item rating matrix… More >

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