Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Analysis and Applications of Complex Social Networks

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 32309

Share This Special Issue

Special Issue Editors

Prof. Dr. Piotr Bródka

E-Mail Website
Guest Editor

Department of Computational Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Interests: network science diffusion processes, multilayer networks

Prof. Dr. Katarzyna Musial

E-Mail Website
Guest Editor

School of Software, Faculty of Engineering and IT, University of Technology Sydney, Broadway, NSW 2007, Australia
Interests: complex networks; network dynamics; network motifs

Dr. Radoslaw Michalski

E-Mail Website
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

E-Mail Website
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 various aspects of complex social networks as well as applications of complex network analysis. Our goal is to provide the audience with the state of the art in a number of crucial directions related to complex networks:

Topological analysis;
Complex network dynamics;
Multilayer complex networks;
Spreading and diffusion processes in networks (e.g., social influence);
The applications of complex network analysis.

This Special Issue is intended to be a cross-domain knowledge exchange, which is why we are willing to present the state of the art and current research in the aforementioned areas from different perspectives, such as computer science, physics, and mathematics, but also marketing, sociology or psychology, making this event interdisciplinary yet focused on complex social networks.

The last two decades have brought nearly infinite opportunities regarding collecting information about the behavior of humans. This information has opened up huge opportunities for the domain of complex network analysis that intends to analyze systems as networks. The purpose of this Special Issue is therefore to investigate multiple phenomena found at different levels of complex social networks to provide researchers with new insights and answers to open research questions. We are not limiting researchers to focusing only on fundamental research—applied research is also welcome.

As the era of static network analysis is now moving toward dynamic network analysis, it is a topic of great importance to observe the dynamics of networks as well as dynamic processes, such as the spread of influence, in order to better understand 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. Similar research challenges apply to other kinds of networks, e.g., multilayer networks.

We believe that only by taking advantage of interdisciplinary research in this domain, it is possible to move forward in understanding how complex social networks work and how society may benefit from understanding them better.

This Special Issue is associated with The Workshop on Social Network Analysis in Applications (SNAA, http://snaa.pwr.edu.pl/) and The Workshop on Social Influence (SI, https://wosinf.org), which are held annually in conjunction with the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

The authors of selected papers will be invited to submit extended versions of their conference papers to this Special Issue of Entropy, 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.

Prof. Dr. Piotr Bródka
Prof. Dr. Katarzyna Musial
Dr. Radoslaw Michalski
Prof. Dr. Jarosław Jankowski
Prof. 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. 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.

Keywords

complex networks
network dynamics
multilayer networks
diffusion processes
social influence

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

18 pages, 401 KiB

Open AccessArticle

Exploring Spillover Effects for COVID-19 Cascade Prediction

by Ninghan Chen, Xihui Chen, Zhiqiang Zhong and Jun Pang

Entropy 2022, 24(2), 222; https://doi.org/10.3390/e24020222 - 31 Jan 2022

Cited by 4 | Viewed by 2898

Abstract

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

17 pages, 893 KiB

Open AccessArticle

IKN-CF: An Approach to Identify Key Nodes in Inter-Domain Routing Systems Based on Cascading Failures

by Wendian Zhao, Yongjie Wang, Xinli Xiong and Jiazhen Zhao

Entropy 2021, 23(11), 1456; https://doi.org/10.3390/e23111456 - 2 Nov 2021

Cited by 5 | Viewed by 2133

Abstract

Inter-domain routing systems is an important complex network in the Internet. Research on the vulnerability of inter-domain routing network nodes is of great support to the stable operation of the Internet. For the problem of node vulnerability, we proposed a method for identifying key nodes in inter-domain routing systems based on cascading failures (IKN-CF). Firstly, we analyzed the topology of inter-domain routing network and proposed an optimal valid path discovery algorithm considering business relationships. Then, the reason and propagation mechanism of cascading failure in the inter-domain routing network were analyzed, and we proposed two cascading indicators, which can approximate the impact of node failure on the network. After that, we established a key node identification model based on improved entropy weight TOPSIS (EWT), and the key node sequence in the network can be obtained through EWT calculation. We compared the existing three methods in two real inter-domain routing networks. The results indicate that the ranking results of IKN-CF are high accuracy, strong stability, and wide applicability. The accuracy of the top 100 nodes of the ranking result can reach 83.6%, which is at least 12.8% higher than the average accuracy of the existing three methods. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

18 pages, 3163 KiB

Open AccessArticle

Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations

by Alexandros Z. Spyropoulos, Charalampos Bratsas, Georgios C. Makris, Evangelos Ioannidis, Vassilis Tsiantos and Ioannis Antoniou

Entropy 2021, 23(10), 1334; https://doi.org/10.3390/e23101334 - 13 Oct 2021

Cited by 11 | Viewed by 4031

Abstract

In recent years, law enforcement authorities have increasingly used mathematical tools to support criminal investigations, such as those related to terrorism. In this work, two relevant questions are discussed: “How can the different roles of members of a terrorist organization be recognized?” and “are there early signs of impending terrorist acts?” These questions are addressed using the tools of entropy and network theory, more specifically centralities (degree, betweenness, clustering) and their entropies. These tools were applied to data (physical contacts) of four real terrorist networks from different countries. The different roles of the members are clearly recognized from the values of the selected centralities. An early sign of impending terrorist acts is the evolutionary pattern of the values of the entropies of the selected centralities. These results have been confirmed in all four terrorist networks. The conclusion is expected to be useful to law enforcement authorities to identify the roles of the members of terrorist organizations as the members with high centrality and to anticipate when a terrorist attack is imminent, by observing the evolution of the entropies of the centralities. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

21 pages, 4579 KiB

Open AccessArticle

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

by Shaojuan Lei, Xiaodong Zhang and Suhui Liu

Entropy 2021, 23(9), 1235; https://doi.org/10.3390/e23091235 - 21 Sep 2021

Cited by 6 | Viewed by 2613

Abstract

A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

18 pages, 906 KiB

Open AccessArticle

An Efficient Partition-Based Approach to Identify and Scatter Multiple Relevant Spreaders in Complex Networks

by Jedidiah Yanez-Sierra, Arturo Diaz-Perez and Victor Sosa-Sosa

Entropy 2021, 23(9), 1216; https://doi.org/10.3390/e23091216 - 15 Sep 2021

Cited by 3 | Viewed by 2499

Abstract

One of the main problems in graph analysis is the correct identification of relevant nodes for spreading processes. Spreaders are crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases, rumors, and more. Correct identification of spreaders in graph analysis is a relevant task to optimally use the network structure and ensure a more efficient flow of information. Additionally, network topology has proven to play a relevant role in the spreading processes. In this sense, more of the existing methods based on local, global, or hybrid centrality measures only select relevant nodes based on their ranking values, but they do not intentionally focus on their distribution on the graph. In this paper, we propose a simple yet effective method that takes advantage of the underlying graph topology to guarantee that the selected nodes are not only relevant but also well-scattered. Our proposal also suggests how to define the number of spreaders to select. The approach is composed of two phases: first, graph partitioning; and second, identification and distribution of relevant nodes. We have tested our approach by applying the SIR spreading model over nine real complex networks. The experimental results showed more influential and scattered values for the set of relevant nodes identified by our approach than several reference algorithms, including degree, closeness, Betweenness, VoteRank, HybridRank, and IKS. The results further showed an improvement in the propagation influence value when combining our distribution strategy with classical metrics, such as degree, outperforming computationally more complex strategies. Moreover, our proposal shows a good computational complexity and can be applied to large-scale networks. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

18 pages, 5111 KiB

Open AccessArticle

Robustness of Cyber-Physical Supply Networks in Cascading Failures

by Dong Mu, Xiongping Yue and Huanyu Ren

Entropy 2021, 23(6), 769; https://doi.org/10.3390/e23060769 - 18 Jun 2021

Cited by 10 | Viewed by 3009

Abstract

A cyber-physical supply network is composed of an undirected cyber supply network and a directed physical supply network. Such interdependence among firms increases efficiency but creates more vulnerabilities. The adverse effects of any failure can be amplified and propagated throughout the network. This paper aimed at investigating the robustness of the cyber-physical supply network against cascading failures. Considering that the cascading failure is triggered by overloading in the cyber supply network and is provoked by underload in the physical supply network, a realistic cascading model for cyber-physical supply networks is proposed. We conducted a numerical simulation under cyber node and physical node failure with varying parameters. The simulation results demonstrated that there are critical thresholds for both firm’s capacities, which can determine whether capacity expansion is helpful; there is also a cascade window for network load distribution, which can determine the cascading failures occurrence and scale. Our work may be beneficial for developing cascade control and defense strategies in cyber-physical supply networks. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

43 pages, 13388 KiB

Open AccessArticle

Detecting and Analyzing Politically-Themed Stocks Using Text Mining Techniques and Transfer Entropy—Focus on the Republic of Korea’s Case

by Insu Choi and Woo Chang Kim

Entropy 2021, 23(6), 734; https://doi.org/10.3390/e23060734 - 9 Jun 2021

Cited by 11 | Viewed by 4288

Abstract

Politically-themed stocks mainly refer to stocks that benefit from the policies of politicians. This study gave the empirical analysis of the politically-themed stocks in the Republic of Korea and constructed politically-themed stock networks based on the Republic of Korea’s politically-themed stocks, derived mainly from politicians. To select politically-themed stocks, we calculated the daily politician sentiment index (PSI), which means politicians’ daily reputation using politicians’ search volume data and sentiment analysis results from politician-related text data. Additionally, we selected politically-themed stock candidates from politician-related search volume data. To measure causal relationships, we adopted entropy-based measures. We determined politically-themed stocks based on causal relationships from the rates of change of the PSI to their abnormal returns. To illustrate causal relationships between politically-themed stocks, we constructed politically-themed stock networks based on causal relationships using entropy-based approaches. Moreover, we experimented using politically-themed stocks in real-world situations from the schematized networks, focusing on politically-themed stock networks’ dynamic changes. We verified that the investment strategy using the PSI and politically-themed stocks that we selected could benchmark the main stock market indices such as the KOSPI and KOSDAQ around political events. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

23 pages, 5776 KiB

Open AccessArticle

Analyzing the Coevolution of Mobile Application Diffusion and Social Network: A Multi-Agent Model

by Zhenyu Zhang, Huirong Zhang, Lixin Zhou and Yanfeng Li

Entropy 2021, 23(5), 521; https://doi.org/10.3390/e23050521 - 24 Apr 2021

Cited by 10 | Viewed by 2687

Abstract

The successful diffusion of mobile applications in user groups can establish a good image for enterprises, gain a good reputation, fight for market share, and create commercial profits. Thus, it is of great significance for the successful diffusion of mobile applications to study mobile application diffusion and social network coevolution. Firstly, combined with a social network’s dynamic change characteristics in real life, a mobile application users’ social network evolution mechanism was designed. Then, a multi-agent model of the coevolution of a social network and mobile application innovation diffusion was constructed. Finally, the impact of mobile applications’ value perception revenue, use cost, marketing promotion investment, and the number of seed users on the coevolution of social network and mobile application diffusion were analyzed. The results show that factors such as the network structure, the perceived value income, the cost of use, the marketing promotion investment, and the number of seed users have an important impact on mobile application diffusion. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

22 pages, 3543 KiB

Open AccessArticle

Dynamic Robustness of Semantic-Based Collaborative Knowledge Network of Open Source Project

by Shaojuan Lei, Xiaodong Zhang, Shilin Xie and Xin Zheng

Entropy 2021, 23(4), 391; https://doi.org/10.3390/e23040391 - 25 Mar 2021

Cited by 6 | Viewed by 2084

Abstract

Robustness of the collaborative knowledge network (CKN) is critical to the success of open source projects. To study this robustness more comprehensively and accurately, we constructed a weighted CKN based on the semantic analysis of collaborative behavior, where (a) open source designers were the network nodes, (b) collaborative behavior among designers was the edges, and (c) collaborative text content intensity and collaborative frequency intensity were the edge weights. To study the robustness from a dynamic viewpoint, we constructed three CKNs from different stages of the project life cycle: the start-up, growth and maturation stages. The connectivity and collaboration efficiency of the weighted network were then used as robustness evaluation indexes. Further, we designed four edge failure modes based on the behavioral characteristics of open source designers. Finally, we carried out dynamic robustness analysis experiments based on the empirical data of a Local Motors open source car design project. Our results showed that the CKN performed differently at different stages of the project life cycle, and our specific findings could help community managers of open source projects to formulate different network protection strategies at different stages of their projects. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Figure 1

26 pages, 1219 KiB

Open AccessArticle

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

by Chathurani Senevirathna, Chathika Gunaratne, William Rand, Chathura Jayalath and Ivan Garibay

Entropy 2021, 23(2), 160; https://doi.org/10.3390/e23020160 - 28 Jan 2021

Cited by 9 | Viewed by 3707

Abstract

Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users. Full article

(This article belongs to the Special Issue Analysis and Applications of Complex Social Networks)

► Show Figures

Journal Menu

Journal Browser

Analysis and Applications of Complex Social Networks

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI