Artificial Intelligence in Complex Networks (2nd Edition)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 18 May 2024 | Viewed by 2252

Special Issue Editors


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Department of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China
Interests: complex network; social network analysis; data mining and artificial intelligence
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Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, Messina, Italy
Interests: network science; criminal networks; machine learning; data science; social network analysis
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Guest Editor
Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy
Interests: network science; graph mining; community detection in graphs; recommender systems; trust in virtual communities
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Guest Editor
Department of Mathematics and Informatics, University of Palermo, Palermo, Italy
Interests: social network analysis; complex networks; network science; criminal networks
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Special Issue Information

Dear Colleagues, 

Complex networks offer a unified approach to the study of real-world entities and their connections. Examples of complex networks can be found in many fields of science, such as biological systems, economic systems, and social systems.

In recent years, there has been a significant upsurge of interest in the application of artificial intelligence methods to the study of complex networks. In this context can be ascribed, for example, the suggestion of new connections between entities, the discovery of patterns, and the emergence of structures.

This Special Issue welcomes theoretical and experimental contributions in the area of artificial intelligence applications of complex networks. Areas of interest include, but are not limited to, the following topics:

  • Link prediction;
  • Maximum likelihood;
  • Artificial intelligence methods in complex networks;
  • Artificial intelligence methods in criminal networks;
  • Community detection;
  • Network mining;
  • Methods for the analysis of network structures.

Prof. Dr. Xiaoyang Liu
Dr. Giacomo Fiumara
Dr. Pasquale De Meo
Dr. Annamaria Ficara
Guest Editors

Manuscript Submission Information

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Keywords

  • maximum likelihood
  • artificial intelligence methods in complex networks
  • artificial intelligence methods in criminal networks
  • community detection
  • network mining
  • methods for the analysis of network structures

Published Papers (3 papers)

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Editorial

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4 pages, 171 KiB  
Editorial
Special Issue “Artificial Intelligence in Complex Networks”
by Xiaoyang Liu
Appl. Sci. 2024, 14(7), 2822; https://doi.org/10.3390/app14072822 - 27 Mar 2024
Viewed by 300
Abstract
Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...] Full article
(This article belongs to the Special Issue Artificial Intelligence in Complex Networks (2nd Edition))

Research

Jump to: Editorial

26 pages, 14493 KiB  
Article
A Novel Method to Identify Key Nodes in Complex Networks Based on Degree and Neighborhood Information
by Na Zhao, Shuangping Yang, Hao Wang, Xinyuan Zhou, Ting Luo and Jian Wang
Appl. Sci. 2024, 14(2), 521; https://doi.org/10.3390/app14020521 - 07 Jan 2024
Cited by 1 | Viewed by 635
Abstract
One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a [...] Read more.
One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a balance between time efficiency and accuracy, while the majority of research neglects the significance of local clustering coefficients, a crucial node property. Thus, this paper introduces a centrality metric called DNC (degree and neighborhood information centrality) that considers both node degree and local clustering coefficients. The combination of these two aspects provides DNC with the ability to create a more comprehensive measure of nodes’ local centrality. In addition, in order to obtain better performance in different networks, this paper sets a tunable parameter α to control the effect of neighbor information on the importance of nodes. Subsequently, the paper proceeds with a sequence of experiments, including connectivity tests, to validate the efficacy of DNC. The results of the experiments demonstrate that DNC captures more information and outperforms the other eight centrality metrics. Full article
(This article belongs to the Special Issue Artificial Intelligence in Complex Networks (2nd Edition))
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21 pages, 2866 KiB  
Article
Sentiment Analysis of Students’ Feedback on E-Learning Using a Hybrid Fuzzy Model
by Maryam Alzaid and Fethi Fkih
Appl. Sci. 2023, 13(23), 12956; https://doi.org/10.3390/app132312956 - 04 Dec 2023
Viewed by 860
Abstract
It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students [...] Read more.
It is crucial to analyze opinions about the significant shift in education systems around the world, because of the widespread use of e-learning, to gain insight into the state of education today. A particular focus should be placed on the feedback from students regarding the profound changes they experience when using e-learning. In this paper, we propose a model that combines fuzzy logic with bidirectional long short-term memory (BiLSTM) for the sentiment analysis of students’ textual feedback on e-learning. We obtained this feedback from students’ tweets expressing their opinions about e-learning. There were some ambiguous characteristics in terms of the writing style and language used in the collected feedback. It was written informally and not in adherence to standardized Arabic language writing rules by using the Saudi dialects. The proposed model benefits from the capabilities of the deep neural network BiLSTM to learn and also from the ability of fuzzy logic to handle uncertainties. The proposed models were evaluated using the appropriate evaluation metrics: accuracy, F1-score, precision, and recall. The results showed the effectiveness of our proposed model and that it worked well for analyzing opinions obtained from Arabic texts written in Saudi dialects. The proposed model outperformed the compared models by obtaining an accuracy of 86% and an F1-score of 85%. Full article
(This article belongs to the Special Issue Artificial Intelligence in Complex Networks (2nd Edition))
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