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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

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

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

Jump to: Research

4 pages, 171 KiB

Open AccessEditorial

Special Issue “Artificial Intelligence in Complex Networks”

by Xiaoyang Liu

Appl. Sci. 2024, 14(7), 2822; https://0-doi-org.brum.beds.ac.uk/10.3390/app14072822 - 27 Mar 2024

Viewed by 344

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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app14020521 - 07 Jan 2024

Cited by 1 | Viewed by 684

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 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

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/app132312956 - 04 Dec 2023

Viewed by 914

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 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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