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  • 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 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 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. More >

  • Open Access

    ARTICLE

    Topic Evolution Analysis in Social Networking Services: Taking Sina Weibo as an Example

    Yuhui Wang

    Computer Systems Science and Engineering, Vol.33, No.4, pp. 287-291, 2018, DOI:10.32604/csse.2018.33.287

    Abstract Event-related topics in social networking services are always the epitome of heated society issues, therefore determining the significance of analyzing its evolution patterns. In this paper, we present a comprehensive survey on the tweets about "ransomware" in Sina Weibo, a famous social networking service similar to twitter in China. The keyword corresponds to a global ransomware attack in May 2017, on which our example event-related topics are based. We collect text data from sina Weibo and vectorize each tweets, before using a dynamic topic model to discover the event-related topics. The results of the topic More >

  • Open Access

    ARTICLE

    An Iteration-Based Differentially Private Social Network Data Release

    Tianqing Zhu1, Mengmeng Yang1, Ping Xiong2, Yang Xiang1, Wanlei Zhou1

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 61-69, 2018, DOI:10.32604/csse.2018.33.061

    Abstract Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users’ privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large More >

  • 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… More >

  • Open Access

    ARTICLE

    Seed Selection for Data Offloading Based on Social and Interest Graphs

    Ying Li1, Jianbo Li1,*, Jianwei Chen1, Minchao Lu1, Caoyuan Li2,3

    CMC-Computers, Materials & Continua, Vol.57, No.3, pp. 571-587, 2018, DOI:10.32604/cmc.2018.02851

    Abstract The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user More >

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