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Foundations of Network Analysis
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Foundations of Network Analysis

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

Deadline for manuscript submissions: closed (21 January 2024) | Viewed by 8326

Special Issue Editors

Dr. Marianna Milano

E-Mail Website
Guest Editor
Department of Experimental and Clinical Medicine, University of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics; network analysis; biological data analysis; COVID-19
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Agapito

E-Mail Website
Guest Editor
Department of Legal, Historical, Economic and SocialSciences, University "Magna Græcia" of Catanzaro, 88100 Catanzaro, Italy
Interests: distributed computing; bioinformatics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modelling and investigation of complex system through graphs that integrate biological, biomedical, and clinical data represent a hot topic for the research community.

Networks and network analysis methods are a keystone in computational biology and bioinformatics, and are increasingly used to study biological and clinical data in an integrated way.

Network analysis consists of a collection of techniques with a shared methodological perspective, which allow relations among entities to be depicted and structures that emerge from the recurrence of these relations to be analyzed. Network analysis can be performed  to a single network or for the comparison of two or more networks. These methods enable the network properties that are used to infer knowledge to be analyzed and the conservation and divergence of interactions between different species to be studied.

Biological network analysis can be utilized in several applications such as the identification of drug targets, the determination of the role of proteins or genes of unknown function, the design of effective strategies for infectious diseases, and the early diagnosis of neurological disorders through detecting abnormal patterns of neural synchronization in specific brain regions.

This Special Issue primarily focuses on the collection of advanced works on the development of new pipelines, algorithms, and tools for the network analysis of complex systems in different domains.

Dr. Marianna Milano
Dr. Giuseppe Agapito
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

  • network
  • network analysis
  • network representation learning
  • networks alignment
  • complex prediction
  • network embedding
  • pathways analysis
  • network models
  • network-based bioinformatics methods

Published Papers (5 papers)

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Research

22 pages, 4600 KiB  
Article
Community-Based Matrix Factorization (CBMF) Approach for Enhancing Quality of Recommendations
by Srilatha Tokala, Murali Krishna Enduri, T. Jaya Lakshmi and Hemlata Sharma
Entropy 2023, 25(9), 1360; https://doi.org/10.3390/e25091360 - 20 Sep 2023
Viewed by 956
Abstract
Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial [...] Read more.
Matrix factorization is a long-established method employed for analyzing and extracting valuable insight recommendations from complex networks containing user ratings. The execution time and computational resources demanded by these algorithms pose limitations when confronted with large datasets. Community detection algorithms play a crucial role in identifying groups and communities within intricate networks. To overcome the challenge of extensive computing resources with matrix factorization techniques, we present a novel framework that utilizes the inherent community information of the rating network. Our proposed approach, named Community-Based Matrix Factorization (CBMF), has the following steps: (1) Model the rating network as a complex bipartite network. (2) Divide the network into communities. (3) Extract the rating matrices pertaining only to those communities and apply MF on these matrices in parallel. (4) Merge the predicted rating matrices belonging to communities and evaluate the root mean square error (RMSE). In our experimentation, we use basic MF, SVD++, and FANMF for matrix factorization, and the Louvain algorithm is used for community division. The experimental evaluation on six datasets shows that the proposed CBMF enhances the quality of recommendations in each case. In the MovieLens 100K dataset, RMSE has been reduced to 0.21 from 1.26 using SVD++ by dividing the network into 25 communities. A similar reduction in RMSE is observed for the datasets of FilmTrust, Jester, Wikilens, Good Books, and Cell Phone. Full article
(This article belongs to the Special Issue Foundations of Network Analysis)
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23 pages, 5656 KiB  
Article
Analysis of Weather Factors on Aircraft Cancellation using a Multilayer Complex Network
by Kyunghun Kim, Hoyong Lee, Myungjin Lee, Young Hye Bae, Hung Soo Kim and Soojun Kim
Entropy 2023, 25(8), 1209; https://doi.org/10.3390/e25081209 - 14 Aug 2023
Viewed by 983
Abstract
Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations [...] Read more.
Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations due to weather factors considered both aircraft and weather factors concurrently, the complex network studies only treated the aircraft factor with a single-layer network. Therefore, the aim of this study was to apply a multilayer complex network (MCN) method that incorporated three different factors, namely, aircraft, rainfall, and wind speed, to investigate aircraft cancellations at 14 airports in the Republic of Korea. The results showed that rainfall had a greater impact on aircraft cancellations compared with wind speed. To find out the most important node in the cancellation, we applied centrality analysis based on information entropy. According to the centrality analysis, Jeju Airport was identified as the most influential node since it has a high demand for aircraft. Also, we showed that characteristics and factors of aircraft cancellation should be appropriately defined by links in the MCN. Furthermore, we verified the applicability of the MCN method in the fields of aviation and meteorology. It is expected that the suggested methodology in this study can help to understand aircraft cancellation due to weather factors. Full article
(This article belongs to the Special Issue Foundations of Network Analysis)
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15 pages, 503 KiB  
Article
Identifying Candidate Gene–Disease Associations via Graph Neural Networks
by Pietro Cinaglia and Mario Cannataro
Entropy 2023, 25(6), 909; https://doi.org/10.3390/e25060909 - 07 Jun 2023
Cited by 5 | Viewed by 2151
Abstract
Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene–disease [...] Read more.
Real-world objects are usually defined in terms of their own relationships or connections. A graph (or network) naturally expresses this model though nodes and edges. In biology, depending on what the nodes and edges represent, we may classify several types of networks, gene–disease associations (GDAs) included. In this paper, we presented a solution based on a graph neural network (GNN) for the identification of candidate GDAs. We trained our model with an initial set of well-known and curated inter- and intra-relationships between genes and diseases. It was based on graph convolutions, making use of multiple convolutional layers and a point-wise non-linearity function following each layer. The embeddings were computed for the input network built on a set of GDAs to map each node into a vector of real numbers in a multidimensional space. Results showed an AUC of 95% for training, validation, and testing, that in the real case translated into a positive response for 93% of the Top-15 (highest dot product) candidate GDAs identified by our solution. The experimentation was conducted on the DisGeNET dataset, while the DiseaseGene Association Miner (DG-AssocMiner) dataset by Stanford’s BioSNAP was also processed for performance evaluation only. Full article
(This article belongs to the Special Issue Foundations of Network Analysis)
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12 pages, 5357 KiB  
Article
Ranking Plant Network Nodes Based on Their Centrality Measures
by Nilesh Kumar and M. Shahid Mukhtar
Entropy 2023, 25(4), 676; https://doi.org/10.3390/e25040676 - 18 Apr 2023
Cited by 5 | Viewed by 1996
Abstract
Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help [...] Read more.
Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein–protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant–pathogen interactions and related functions and pathways compared to the other algorithms. Full article
(This article belongs to the Special Issue Foundations of Network Analysis)
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14 pages, 468 KiB  
Article
A Method Based on Temporal Embedding for the Pairwise Alignment of Dynamic Networks
by Pietro Cinaglia and Mario Cannataro
Entropy 2023, 25(4), 665; https://doi.org/10.3390/e25040665 - 15 Apr 2023
Cited by 9 | Viewed by 1222
Abstract
In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of [...] Read more.
In network analysis, real-world systems may be represented via graph models, where nodes and edges represent the set of biological objects (e.g., genes, proteins, molecules) and their interactions, respectively. This representative knowledge-graph model may also consider the dynamics involved in the evolution of the network (i.e., dynamic networks), in addition to a classic static representation (i.e., static networks). Bioinformatics solutions for network analysis allow knowledge extraction from the features related to a single network of interest or by comparing networks of different species. For instance, we may align a network related to a well known species to a more complex one in order to find a match able to support new hypotheses or studies. Therefore, the network alignment is crucial for transferring the knowledge between species, usually from simplest (e.g., rat) to more complex (e.g., human). Methods: In this paper, we present Dynamic Network Alignment based on Temporal Embedding (DANTE), a novel method for pairwise alignment of dynamic networks that applies the temporal embedding to investigate the topological similarities between the two input dynamic networks. The main idea of DANTE is to consider the evolution of interactions and the changes in network topology. Briefly, the proposed solution builds a similarity matrix by integrating the tensors computed via the embedding process and, subsequently, it aligns the pairs of nodes by performing its own iterative maximization function. Results: The performed experiments have reported promising results in terms of precision and accuracy, as well as good robustness as the number of nodes and time points increases. The proposed solution showed an optimal trade-off between sensitivity and specificity on the alignments produced on several noisy versions of the dynamic yeast network, by improving by ∼18.8% (with a maximum of 20.6%) the Area Under the Receiver Operating Characteristic (ROC) Curve (i.e., AUC or AUROC), compared to two well known methods: DYNAMAGNA++ and DYNAWAVE. From the point of view of quality, DANTE outperformed these by ∼91% as nodes increase and by ∼75% as the number of time points increases. Furthermore, a ∼23.73% improvement in terms of node correctness was reported with our solution on real dynamic networks. Full article
(This article belongs to the Special Issue Foundations of Network Analysis)
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