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Computational Social Networks
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  3. Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can potentially be ...

    Authors: Alexander Semenov, Alexander Veremyev, Alexander Nikolaev, Eduardo L. Pasiliao and Vladimir Boginski

    Citation: Computational Social Networks 2020 7:1

    Content type: Research

    Published on:

  4. We consider that a network of chaotic identical dynamical systems is connected to a new node. Depending on some properties of the network and on the way that connection is made, the new node may control the ne...

    Authors: Clara Grácio, Sara Fernandes and Luís Mário Lopes

    Citation: Computational Social Networks 2019 6:14

    Content type: Research

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  5. The formation of robust communication networks between independently acting agents is of practical interest in multiple domains, for example, in sensor placement and Unmanned Aerial Vehicle communication. Thes...

    Authors: Christopher Diaz, Alexander Nikolaev, Abhinav Perla, Alexander Veremyev and Eduardo Pasiliao

    Citation: Computational Social Networks 2019 6:13

    Content type: Research

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  6. Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of int...

    Authors: Vincenza Carchiolo, Marco Grassia, Alessandro Longheu, Michele Malgeri and Giuseppe Mangioni

    Citation: Computational Social Networks 2019 6:12

    Content type: Research

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  7. Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, ...

    Authors: Si Zhang, Hanghang Tong, Jiejun Xu and Ross Maciejewski

    Citation: Computational Social Networks 2019 6:11

    Content type: Research

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  8. Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the populatio...

    Authors: Han Hu, NhatHai Phan, Soon A. Chun, James Geller, Huy Vo, Xinyue Ye, Ruoming Jin, Kele Ding, Deric Kenne and Dejing Dou

    Citation: Computational Social Networks 2019 6:10

    Content type: Research

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  9. In the standard situation of networked populations, link neighbours represent one of the main influences leading to social diffusion of behaviour. When distinct attributes coexist, not only the network structu...

    Authors: Marie Lisa Kapeller, Georg Jäger and Manfred Füllsack

    Citation: Computational Social Networks 2019 6:9

    Content type: Research

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  11. 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

    Content type: Research

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  12. 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

    Content type: Research

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  13. 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

    Content type: Research

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  15. 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

    Content type: Research

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  17. 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

    Content type: Research

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  19. 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

    Content type: Research

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  21. 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

    Content type: Research

    Published on:

  25. 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

    Content type: Research

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  26. 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

    Content type: Research

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  27. 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

    Content type: Research

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  28. 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

    Content type: Research

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  30. 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

    Content type: Research

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  31. 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

    Content type: Research

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  34. 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

    Content type: Research

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  36. 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

    Content type: Research

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  40. 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

    Content type: Research

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  42. 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

    Content type: Research

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  44. 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

    Content type: Research

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  48. 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

    Content type: Research

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  49. 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

    Content type: Research

    Published on:

  50. 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

    Content type: Research

    Published on:

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  • ISSN: 2197-4314 (electronic)

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