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Information
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Social Influence
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Social Influence

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

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 4307

Special Issue Editors

Dr. Radoslaw Michalski

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Guest Editor
Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland
Interests: diffusion processes in social networks; temporal networks; machine learning for analyzing the abovementioned phenomena; blockchain solutions and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jarosław Jankowski

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Guest Editor
Department of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, Szczecin, Poland
Interests: information diffusion; decision support systems; sustainability; human-computer interaction; online marketing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Shakarian

E-Mail Website
Guest Editor
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ‎, USA
Interests: cyber security; social networks; AI; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to gather researchers studying the phenomenon of social influence and information spreading in networks, and it is indented to be a cross-domain knowledge exchange. That is why we are willing to present the state of the art and current research in this area from different perspectives: sociology, computer science, psychology, as well as mathematics and physics, making this event interdisciplinary.

The digital era delivers new possibilities for analyzing humans’ behavior, especially as it is expressed on the Internet, as well as through the traces we leave when making phone calls or using GPS devices or any kind of transportation.

People who meet together or communicate over the Internet or other channels constantly exchange information, rumors, spread opinions and attitudes. Sometimes it is just a piece of information that is being passed from one to another, but it may also become the beginning of a huge change, either for an individual or even for the whole of society—but in both cases, it starts with someone becoming influenced or influencing others.

The main topic of our interest, social influence, is the process of a complex nature which involves our location in a social network, the network structure and dynamics, and time and psychological and sociological factors.

At the level of an individual, it is rather a psychological process, but at the network scale, it is strongly dependent on the network structure and its dynamics. Hence, studying social influence is a challenging interdisciplinary task, which, if successful, will lead to a better understanding of the surrounding world.

The goal of this Special Issue devoted to the social influence process is to present the research on:

  • how the social influence occurs in society at the level of an individual and at the network level (empirical research);
  • how to simplify or find theoretical representation for this complex phenomenon (models);
  • how to target the society to maximize the spread of influence or innovations diffusion (heuristics, analytical solutions);
  • how to influence individuals (psychological and sociological factors, the impact of social media on the influence);
  • how to detect, quantify, and prevent content manipulation aimed at influencing individuals;
  • how to achieve different goals related to social influence, like minimizing the cost of change or slowing down or speeding up this process.

As the era of static networks analysis is now moving towards dynamic networks analysis, it is a topic of great importance to observe the dynamics of the networks as well as the dynamic processes, like the spread of influence, in order to better understanding of the human behavior. Here, the dynamics is being observed at two levels: The social network itself changes, and this network becomes a transmission layer for another dynamic process—spread of influence. This is why there is still an open debate around what plays a more important role—the underlying layer or the social influence process itself. By organizing this Special Issue, we would like to come closer to an answer on this question.

We believe that only by taking the advantage of all the abovementioned fields is it possible to move forward in understanding how this complex works and how society may benefit from understanding it better.

This Special Issue is associated with The 5th Workshop on Social Influence, which will be held in Vancouver, Canada (more info at https://www.wosinf.org) in conjunction with the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (more info at http://asonam.cpsc.ucalgary.ca/2019/) on 27th August 2019.

The authors of selected papers will be invited to submit extended versions of their conference papers to this Special Issue of Information, published by MDPI, in open access format.

This call for papers is also fully open to all who want to contribute by submitting a relevant research manuscript.

Dr. Radoslaw Michalski
Dr. Jarosław Jankowski
Dr. Paulo Shakarian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

  • social influence
  • information spreading
  • social networks
  • complex networks

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Published Papers (1 paper)

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Research

23 pages, 2268 KiB  
Article
Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data †
by Nikolaus Nova Parulian, Tiffany Lu, Shubhanshu Mishra, Mihai Avram and Jana Diesner
Information 2020, 11(6), 306; https://doi.org/10.3390/info11060306 - 6 Jun 2020
Cited by 2 | Viewed by 3612
Abstract
Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the [...] Read more.
Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the domain of social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of a target node according to centrality measures. This can represent the attempt of an adversary to boost or demote the degree to which others perceive individual nodes as influential or powerful. We study the impact of adversarial attacks on targets and victims, and identify metric-based security strategies to mitigate such attacks. We conduct a series of controlled experiments on synthetic network data to identify attacks that allow the adversary to achieve their objective with a single move. We then replicate the experiments with empirical network data. We run our experiments on common network topologies and use common centrality measures. We identify a small set of moves that result in the adversary achieving their objective. This set is smaller for decreasing centrality measures than for increasing them. For both synthetic and empirical networks, we observe that larger networks are less prone to adversarial attacks than smaller ones. Adversarial moves have a higher impact on cellular and small-world networks, while random and scale-free networks are harder to perturb. Also, empirical networks are harder to attack than synthetic networks. Using correlation analysis on our experimental results, we identify how combining measures with low correlation can aid in reducing the effectiveness of adversarial moves. Our results also advance the knowledge about the robustness of centrality measures to network perturbations. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and developing solutions to improving network security. Full article
(This article belongs to the Special Issue Social Influence)
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