Special Issue "Computational Social Science"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 15 May 2019

Special Issue Editors

Guest Editor
Prof. Dr. Kimmo Kaski

Department of Computer Science, Aalto University School of Science, P.O.Box 15500, FI-00076 AALTO, Finland
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Interests: computational science; statistical physics; complex systems; complex networks; data science; computational social science
Guest Editor
Prof. Dr. Hang-Hyun Jo

Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea
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Interests: statistical physics; complex systems; networks; bursty dynamics
Guest Editor
Prof. Dr. Gerardo Iñiguez

Department of Network and Data Science, Central European University, Nador u. 9, 1051 Budapest, Hungary
Website | E-Mail
Interests: complex systems; network science; computational social science

Special Issue Information

Dear Colleagues,

The last centuries have seen a great surge in our understanding and control of ‘simple’ physical, chemical, and biological processes through data analysis and the mathematical modelling of their underlying dynamics. Encouraged by its success, researchers have recently embarked on extending such approaches to gain qualitative and quantitative understanding of social and economic systems and the dynamics in and of them. This has become possible due to the massive amounts of data generated by information-communication technologies and the unprecedented fusion of off- and on-line human activity. However, due to the presence of adaptability, feedback loops, and strong heterogeneities of the individuals and interactions making up our modern digital societies, it is yet unclear if statistical ‘laws’ of socio-technical behaviour even exist, akin to those found for natural processes. Such continuing search has resulted in the fields of computational social science and social network science, which share the goal of first analysing social phenomena and then modelling them with enough accuracy to make reliable predictions. This Special Issue invites contributions to such fields of study, with focus on the temporal evolution and dynamics of complex social systems. As topics of interest, we propose research on more realistic models of social dynamics, the use of statistical inference, machine learning, and other cross-disciplinary techniques to complement the analysis of social dynamics, and the creation of loops between data acquisition and model analysis to increase accuracy in the prediction of social trends. We hope this Special Issue will bring together expertise from a wide range of research communities interested in similar topics, including computational social science, network science, information science, and complexity science.

Prof. Dr. Kimmo Kaski
Prof. Dr. Hang-Hyun Jo
Prof. Dr. Gerardo Iñiguez
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational social science
  • complex social systems and dynamics
  • social network analysis and modeling
  • collective dynamics in social and economic systems
  • social physics and econophysics
  • temporally evolving social networks
  • online and offline social networks
  • information diffusion, social contagion, and opinion formation
  • collective intelligence
  • crowd-sourcing; herding behavior vs. wisdom of crowds
  • human mobility and transportation
  • group formation, evolution and group behavior analysis
  • modeling, tracking, and forecasting dynamic groups in social media
  • community detection and dynamic community structure analysis
  • social simulation and computing
  • empirical calibration and validation of agent-based models
  • coevolution of network and behavior

Published Papers (3 papers)

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Research

Open AccessArticle Dynamic Evolution Model of a Collaborative Innovation Network from the Resource Perspective and an Application Considering Different Government Behaviors
Information 2019, 10(4), 138; https://doi.org/10.3390/info10040138
Received: 22 February 2019 / Revised: 6 April 2019 / Accepted: 11 April 2019 / Published: 12 April 2019
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Abstract
The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of [...] Read more.
The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. This paper summarizes the three characteristics of collaborative innovation networks, knowledge transfer, policy environment, and periodic cooperation, and it establishes a dynamic evolution model for a resource-priority connection mechanism based on innovation resource theory. The network subjects are not randomly testing all of the potential partners, but have a strong tendency to, which is, innovation resource. The evolution process of a collaborative innovation network is simulated with three different government behaviors as experimental objects. The evolution results show that the government should adopt the policy of supporting the enterprises that recently entered the network, which can maintain the innovation vitality of the network and benefit the innovation output. The results of this study also provide a reference for decision-making by the government and enterprises. Full article
(This article belongs to the Special Issue Computational Social Science)
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Open AccessArticle Analysis of SAP Log Data Based on Network Community Decomposition
Information 2019, 10(3), 92; https://doi.org/10.3390/info10030092
Received: 27 January 2019 / Revised: 18 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
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Abstract
Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process [...] Read more.
Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case. Full article
(This article belongs to the Special Issue Computational Social Science)
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Open AccessArticle Axiomatisation and Simulation
Information 2019, 10(2), 53; https://doi.org/10.3390/info10020053
Received: 12 December 2018 / Revised: 15 January 2019 / Accepted: 2 February 2019 / Published: 6 February 2019
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Abstract
The paper discusses the relation between the “non-statement view” of the structuralist program in philosophy of science and agent-based simulation and the use of this relation for a deeper understanding of the verification and the validation of simulation models. To this end it [...] Read more.
The paper discusses the relation between the “non-statement view” of the structuralist program in philosophy of science and agent-based simulation and the use of this relation for a deeper understanding of the verification and the validation of simulation models. To this end it uses the history of the gender desegregation process in German schools in the second half of the 20th century and two simulation models trying to explain and understand this historical process. The relation between the two simulation models on one hand and the structuralist reconstruction of the mental and verbal theory of the observed phenomenon is depicted step by step, showing the verification of the more recent simulation model along the lines of the formal definition of this theory. Finally, the simulation model is used to make two unobservable parameters measurable with the help of the formalised theory, which allows new insights into the historical process. Full article
(This article belongs to the Special Issue Computational Social Science)
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