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Entropy
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Social Networks and Information Diffusion II
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Social Networks and Information Diffusion II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 14582

Special Issue Editor

Prof. Paolo Pin

E-Mail Website
Guest Editor
Dipartimento di Economia Politica e Statistica, Università degli Studi di Siena, Piazza San Francesco 7, 53100 Siena, Italy
Interests: homophily in social networks; game theoretical models of social networks; information in markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social networks of real acquaintances and on the internet have been at the center of interest, in the last twenty years, in sociology, economics, political science, medicine, applied physics, computer science - scholars and common wisdom agree on the fact that they have a clear impact on the everyday life of people. However, when analyzing communication and information flows across a social network, there is still no agreement on an accepted and established theory for, and on how to systematically study, the nature and the causes of the endogenous evolution of the network itself, the detection of communities in the network, and the propagation of information, news, habits and opinions across the network. Those aspects are related one to the other, in non trivial ways, and theoretical models, eventually combined with the analysis of real data, are of enormous help in the understanding of many phenomena with analogous features.

The journal Entropy focuses on information theory, complex system analysis and innovative computational methods, which are in turn fundamental in combining all the contributions that allow us to understand social networks.

Any contribution related to the above questions, shared by scholars from different areas of research, would be of great interest for the interdisciplinary community of researchers who study social networks.

This issue is to continue with the first issue of Social Networks and Information Diffusion.

Prof. Paolo Pin
Guest Editor

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. Entropy 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 2600 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.

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Published Papers (3 papers)

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Research

19 pages, 455 KiB  
Article
Towards Social Capital in a Network Organization: A Conceptual Model and an Empirical Approach
by Saad Alqithami, Rahmat Budiarto, Musaad Alzahrani and Henry Hexmoor
Entropy 2020, 22(5), 519; https://doi.org/10.3390/e22050519 - 1 May 2020
Viewed by 3598
Abstract
Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those [...] Read more.
Due to the complexity of an open multi-agent system, agents’ interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one’s current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open source software project to demonstrate how social capital can be created and measured within this type of organization. The results indicate that the values of social capital are positively proportional towards optimizing agents’ productivity into successful completion of the project. Full article
(This article belongs to the Special Issue Social Networks and Information Diffusion II)
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19 pages, 848 KiB  
Article
Identifying Influencers in Social Networks
by Xinyu Huang, Dongming Chen, Dongqi Wang and Tao Ren
Entropy 2020, 22(4), 450; https://doi.org/10.3390/e22040450 - 15 Apr 2020
Cited by 21 | Viewed by 6738
Abstract
Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has [...] Read more.
Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field. Full article
(This article belongs to the Special Issue Social Networks and Information Diffusion II)
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20 pages, 1055 KiB  
Article
Activeness and Loyalty Analysis in Event-Based Social Networks
by Thanh Trinh, Dingming Wu, Joshua Zhexue Huang and Muhammad Azhar
Entropy 2020, 22(1), 119; https://doi.org/10.3390/e22010119 - 18 Jan 2020
Cited by 9 | Viewed by 3711
Abstract
Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a [...] Read more.
Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features. Full article
(This article belongs to the Special Issue Social Networks and Information Diffusion II)
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