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Entropy
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Dynamics and Entropy in Networked Systems
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Dynamics and Entropy in Networked Systems

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 6884

Special Issue Editor

Dr. Kang Hao Cheong

E-Mail Website1 Website2
Guest Editor
1. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639818, Singapore
2. Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Interests: game theory; extended reality; data science; AI/ML in the medical field/healthcare; pedagogy and educational research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is becoming increasingly connected, from IoT and ‘smart’ devices to social networks and ‘big data’. Networked systems are not just relevant to information technology, they are also moving fast into many engineering applications, medical/healthcare, and cyberphysical system domains. There are many components with complicated interactions in such complex systems, and many of these complex systems, including urban cities, ecosystem, social and economic organizations, the human brain, and ultimately the entire universe, can be well described by complex networks by considering the individual as a node and the relationship as an edge in the network. Therefore, modeling these practical problems using complex networks is an effective approach. Studying the property of networked systems through entropy-based methods, artificial intelligence, and machine learning techniques has attracted significant attention, including individuals’ interaction (game theory), medical and healthcare aspects (decision making), dynamical features (spread of rumor and disease), and system characteristics (reliability and resilience).

This Special Issue offers an opportunity for novel interdisciplinary research and reviews that report on progress in the field of complex systems and improved techniques of entropy-based approaches in complex systems. In particular, the analysis and interpretation of real-world complex systems and engineering applications based on entropy-based, statistical methods and artificial intelligence/machine learning techniques fall within the scope of this Special Issue.

Dr. Kang Hao Cheong
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 system
  • complex network
  • nonlinear dynamics
  • information spread
  • entropy-based method
  • information theory
  • decision making
  • artificial intelligence
  • medical/healthcare
  • engineering applications
  • modeling and simulation

Published Papers (3 papers)

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Research

15 pages, 1455 KiB  
Article
Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks
by Vasily Lubashevskiy, Seval Yurtcicek Ozaydin and Fatih Ozaydin
Entropy 2023, 25(2), 365; https://doi.org/10.3390/e25020365 - 16 Feb 2023
Cited by 1 | Viewed by 1198
Abstract
Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly [...] Read more.
Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties. Full article
(This article belongs to the Special Issue Dynamics and Entropy in Networked Systems)
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13 pages, 618 KiB  
Article
Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks
by Zongjian Yu, Anxiang Zhang, Huali Feng, Huaming Du, Shaopeng Wei and Yu Zhao
Entropy 2022, 24(7), 964; https://doi.org/10.3390/e24070964 - 11 Jul 2022
Viewed by 1400
Abstract
Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. [...] Read more.
Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-generation mechanism. CopyFet can integrate two operations: (i) the regular way of making type inference from the whole type set in the generation model; (ii) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. Despite its simplicity, this mechanism proves to be powerful since extensive experiments show that CopyFet outperforms state-of-the-art methods in FET on two benchmark datasets (FIGER (GOLD) and BBN). For example, CopyFet achieves the new state-of-the-art score of 76.4% and 83.6% on the accuracy metric in FIGER (GOLD) and BBN, respectively. Full article
(This article belongs to the Special Issue Dynamics and Entropy in Networked Systems)
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18 pages, 2760 KiB  
Article
Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images
by Prabal Datta Barua, Wai Yee Chan, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Edward J. Ciaccio, Nazrul Islam, Kang Hao Cheong, Zakia Sultana Shahid and U. Rajendra Acharya
Entropy 2021, 23(12), 1651; https://doi.org/10.3390/e23121651 - 08 Dec 2021
Cited by 23 | Viewed by 3338
Abstract
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT [...] Read more.
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model. Full article
(This article belongs to the Special Issue Dynamics and Entropy in Networked Systems)
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