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Entropy
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Advances in Complex Networks and Artificial Intelligence
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Advances in Complex Networks and Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1134

Special Issue Editor

Dr. Diego Amancio

E-Mail Website
Guest Editor
Department of Computer Science, University of São Paulo, São Paulo 13566-590, Brazil
Interests: research evaluation; network science; science of science; natural language processing; machine learning

Special Issue Information

Dear Colleagues,

Complex networks have found extensive applications in various fields, and the emergence of artificial intelligence has led to the proposal of numerous applications that integrate these approaches. Additionally, networked systems have been characterized and modeled using theory of information and entropy concepts, aiding in the understanding of information networks. The combination of these concepts has been particularly useful in community detection and in comprehending networks generated by sequences, such as texts or time series. Other applications include the use of machine learning tools and complex networks in Science of Science.

This Special Issue is a platform for showcasing studies that merge networked systems, artificial intelligence applications, and concepts from the theory of information and entropy. Specifically, it focuses on the analysis and interpretation of complex systems using both real and artificial datasets, with the support of statistical methods.

Dr. Diego Raphael Amancio
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.

Keywords

  • complex networks
  • machine learning
  • information networks
  • text networks
  • citation networks
  • social networks
  • science of science
  • large language models
  • learning
  • information science.

Published Papers (2 papers)

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Research

20 pages, 681 KiB  
Article
Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models
by Nada Bendahman and Dounia Lotfi
Entropy 2024, 26(6), 486; https://doi.org/10.3390/e26060486 - 31 May 2024
Viewed by 183
Abstract
Abstract: Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals [...] Read more.
Abstract: Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and viruses. Several methods have emerged to pinpoint these influential entities in networks, represented as graphs. In these graphs, nodes correspond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We divide structural centrality into two categories: local, considering a node’s immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both micro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network’s broader features, especially paths of different lengths. Through Spearman and Pearson correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the susceptible–infected–recovered (SIR) model, portraying virus spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model’s progression. Our results indicate a notable correlative efficacy across various real-world networks relative to other centrality metrics. Full article
(This article belongs to the Special Issue Advances in Complex Networks and Artificial Intelligence)
19 pages, 2500 KiB  
Article
Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score
by Y. V. Nandini, T. Jaya Lakshmi, Murali Krishna Enduri and Hemlata Sharma
Entropy 2024, 26(6), 433; https://doi.org/10.3390/e26060433 - 21 May 2024
Viewed by 469
Abstract
Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, [...] Read more.
Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic–Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method. Full article
(This article belongs to the Special Issue Advances in Complex Networks and Artificial Intelligence)
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