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  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. This study examines the communications networks formed by direct international Internet links, weighted by bandwidth capacity, each year over the 2002–2011 period. Specifically, we analyze changes in bandwidth...

    Authors: Hyunjin Seo and Stuart Thorson
    Citation: Computational Social Networks 2015 2:11
  21. Social media and social networks contribute to shape the debate on societal and policy issues, but the dynamics of this process is not well understood. As a case study, we monitor Twitter activity on a wide ra...

    Authors: Borut Sluban, Jasmina Smailović, Stefano Battiston and Igor Mozetič
    Citation: Computational Social Networks 2015 2:9
  22. Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing epidemics and cascades. Often, however, only the infection states of a small set of nodes are ...

    Authors: Yeon-sup Lim, Bruno Ribeiro and Don Towsley
    Citation: Computational Social Networks 2015 2:8
  23. The critical node detection problem (CNDP) aims to fragment a graph G=(V,E) by removing a set of vertices R with cardinality |R|≤k, such that the residual graph has minimum pairwise connectivity for user-defined ...

    Authors: Mario Ventresca and Dionne Aleman
    Citation: Computational Social Networks 2015 2:6
  24. Dealing with big data in computational social networks may require powerful machines, big storage, and high bandwidth, which may seem beyond the capacity of small labs. We demonstrate that researchers with lim...

    Authors: Ming Jia, Hualiang Xu, Jingwen Wang, Yiqi Bai, Benyuan Liu and Jie Wang
    Citation: Computational Social Networks 2015 2:5
  25. Social networks are often degree correlated between nearest neighbors, an effect termed homophily, wherein individuals connect to nearest neighbors of similar connectivity. Whether friendships or other associa...

    Authors: Michael Mayo, Ahmed Abdelzaher and Preetam Ghosh
    Citation: Computational Social Networks 2015 2:4
  26. We study the dynamics of a game-theoretic network formation model that yields large-scale small-world networks. So far, mostly stochastic frameworks have been utilized to explain the emergence of these network...

    Authors: Omid Atabati and Babak Farzad
    Citation: Computational Social Networks 2015 2:1
  27. A lot of daily activities require more than one person to participate and collaborate with each other; however, for many people, it is not easy to find good partners to engage in activities with one another. W...

    Authors: Chunyu Ai, Wei Zhong, Mingyuan Yan and Feng Gu
    Citation: Computational Social Networks 2014 1:5
  28. This paper investigates the influence maximization (IM) problem in social networks under the linear threshold (LT) model. Kempe et al. (ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 137–146...

    Authors: Zaixin Lu, Lidan Fan, Weili Wu, Bhavani Thuraisingham and Kai Yang
    Citation: Computational Social Networks 2014 1:2

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