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Computational Social Networks
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  2. Content type: Research

    Prerequisite inadequacy causes more MOOC drop-out. As an effective method interfering with learning process, existing MOOC recommendation is mainly about subsequent learning objects that have not been learned ...

    Authors: Yanxia Pang, Na Wang, Ying Zhang, Yuanyuan Jin, Wendi Ji and Wenan Tan

    Citation: Computational Social Networks 2019 6:7

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  3. Content type: Research

    In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private att...

    Authors: Tong Yan, Yunpeng Gao and Nan Zhang

    Citation: Computational Social Networks 2019 6:6

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  4. Content type: Research

    In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and ...

    Authors: Quan-Lin Li, Jing-Yu Ma, Yan-Xia Chang, Fan-Qi Ma and Hai-Bo Yu

    Citation: Computational Social Networks 2019 6:5

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  6. Content type: Research

    Unstructured data generated from sources such as the social media and traditional text documents are increasing and form a larger proportion of unanalysed data especially in the developing countries. In this s...

    Authors: Frank Namugera, Ronald Wesonga and Peter Jehopio

    Citation: Computational Social Networks 2019 6:3

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  8. Content type: Research

    Economists and social scientists have studied the human migration extensively. However, the complex network of human mobility in the United States (US) is not studied in depth. In this paper, we analyze migrat...

    Authors: Batyr Charyyev and Mehmet Hadi Gunes

    Citation: Computational Social Networks 2019 6:1

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  10. Content type: Research

    The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense subgraphs inside the network. Here we ...

    Authors: Konstantin E. Avrachenkov, Aleksei Y. Kondratev, Vladimir V. Mazalov and Dmytro G. Rubanov

    Citation: Computational Social Networks 2018 5:11

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  12. Content type: Research

    Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propos...

    Authors: Philipp Lorenz-Spreen, Frederik Wolf, Jonas Braun, Gourab Ghoshal, Nataša Djurdjevac Conrad and Philipp Hövel

    Citation: Computational Social Networks 2018 5:9

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  16. Content type: Research

    Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin–destination data and have difficultie...

    Authors: Christian Hofer, Georg Jäger and Manfred Füllsack

    Citation: Computational Social Networks 2018 5:5

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  17. Content type: Research

    In the framework of network sampling, random walk (RW) based estimation techniques provide many pragmatic solutions while uncovering the unknown network as little as possible. Despite several theoretical advan...

    Authors: Konstantin Avrachenkov, Vivek S. Borkar, Arun Kadavankandy and Jithin K. Sreedharan

    Citation: Computational Social Networks 2018 5:4

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  18. Content type: Research

    Complex networks are found in many domains and the control of these networks is a research topic that continues to draw increasing attention. This paper proposes a method of network control that attempts to ma...

    Authors: Dave McKenney and Tony White

    Citation: Computational Social Networks 2018 5:3

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  19. Content type: Research

    In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profile...

    Authors: Ilire Hasani-Mavriqi, Dominik Kowald, Denis Helic and Elisabeth Lex

    Citation: Computational Social Networks 2018 5:2

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  21. Content type: Research

    In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the i...

    Authors: Pietro DeLellis, Anna DiMeglio, Franco Garofalo and Francesco Lo Iudice

    Citation: Computational Social Networks 2017 4:12

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  22. Content type: Research

    We examine the coevolution of three-layer node-aligned network of university students. The first layer is defined by nominations based on perceived prominence collected from repeated surveys during the first f...

    Authors: Ashwin Bahulkar, Boleslaw K. Szymanski, Kevin Chan and Omar Lizardo

    Citation: Computational Social Networks 2017 4:11

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  25. Content type: Research

    The emerging grassroots party Barcelona en Comú won the 2015 Barcelona City Council election. This candidacy was devised by activists involved in the Spanish 15M movement to transform citizen outrage into poli...

    Authors: Pablo Aragón, Helena Gallego, David Laniado, Yana Volkovich and Andreas Kaltenbrunner

    Citation: Computational Social Networks 2017 4:8

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  27. Content type: Research

    Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit...

    Authors: Miha Grčar, Darko Cherepnalkoski, Igor Mozetič and Petra Kralj Novak

    Citation: Computational Social Networks 2017 4:6

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  31. Content type: Research

    Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users’ individual activities associated with some cost and effort, and thus it is not known ...

    Authors: Kengo Osaka, Fujio Toriumi and Toshihauru Sugawara

    Citation: Computational Social Networks 2017 4:2

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  33. Content type: Research

    Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.

    Authors: Ziyaad Qasem, Marc Jansen, Tobias Hecking and H. Ulrich Hoppe

    Citation: Computational Social Networks 2016 3:11

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  35. Content type: Research

    Why group opinions tend to be converged through continued communication, discussion and interactions? Under the framework of the social influence network model, we rigorously prove that the group consensus is ...

    Authors: Zhenpeng Li, Xijin Tang, Benhui Chen, Jian Yang and Peng Su

    Citation: Computational Social Networks 2016 3:9

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  39. Content type: Research

    Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade...

    Authors: Lobna Azaza, Sergey Kirgizov, Marinette Savonnet, Éric Leclercq, Nicolas Gastineau and Rim Faiz

    Citation: Computational Social Networks 2016 3:5

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  40. Content type: Research

    Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networ...

    Authors: Akmal Artikov, Aleksandr Dorodnykh, Yana Kashinskaya and Egor Samosvat

    Citation: Computational Social Networks 2016 3:4

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  41. Content type: Research

    Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), i...

    Authors: Eriko Otsuka, Scott A. Wallace and David Chiu

    Citation: Computational Social Networks 2016 3:3

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  42. Content type: Research

    In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust i...

    Authors: Yingjie Wang, Zhipeng Cai, Guisheng Yin, Yang Gao, Xiangrong Tong and Qilong Han

    Citation: Computational Social Networks 2016 3:2

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  43. Content type: Research

    Solving the shortest path and min-cut problems are key in achieving high-performance and robust communication networks. Those problems have often been studied in deterministic and uncorrelated networks both i...

    Authors: Song Yang, Stojan Trajanovski and Fernando A. Kuipers

    Citation: Computational Social Networks 2016 3:1

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  44. Content type: Research

    The diffusion of useful information in generalized networks, such as those consisting of wireless physical substrates and social network overlays is very important for theoretical and practical applications. C...

    Authors: Eleni Stai, Vasileios Karyotis and Symeon Papavassiliou

    Citation: Computational Social Networks 2015 2:18

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  46. Content type: Research

    Convenient access to vast and untapped collections of documents generated by organizations is a highly valuable resource for research. These documents (e.g., press releases) are a window into organizational st...

    Authors: Adina Nerghes, Ju-Sung Lee, Peter Groenewegen and Iina Hellsten

    Citation: Computational Social Networks 2015 2:16

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  47. Content type: Research

    Competitiveness is a relevant social behavior and in several contexts, from economy to sport activities, has a fundamental role. We analyze this social behavior in the domain of evolutionary game theory, using...

    Authors: Marco A Javarone and Antonio E Atzeni

    Citation: Computational Social Networks 2015 2:15

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  50. Content type: Research

    We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study extremal properties in any associated stationary sequence of cha...

    Authors: Konstantin Avrachenkov, Natalia M. Markovich and Jithin K. Sreedharan

    Citation: Computational Social Networks 2015 2:12

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  • Speed
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    115 days to first decision for all manuscripts
    262 days from submission to acceptance
    19 days from acceptance to publication

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    26 Altmetric mentions

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  • ISSN: 2197-4314 (electronic)

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