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Complex Networks and Machine Learning: From Molecular to Social Sciences

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

Deadline for manuscript submissions: closed (30 November 2018) | Viewed by 54287

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Special Issue Editors

Prof. Dr. Humbert González-Díaz

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1. Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), 48940 Leioa, Biscay, Spain
2. IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
Interests: cheminformatics; bioinformatics; machine learning; complex networks; computational nanoscience
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Maykel Cruz-Monteagudo

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1. General Education Department, West Coast University, Miami Campus, FL 33136, USA
2. Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Praça de Gomes Teixeira, 4099-002, Portugal
3. Center for Computer Science (CCS), University of Miami, Rosenstiel Medical Science Building (RMSB), 1600 NW 10th Avenue, Miami, FL 33136, USA

Prof. Dr. Terace Fletcher

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Guest Editor

Science Department, West Coast University, Miami Campus, FL 33136, USA

Prof. Dr. David Quesada

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School of Science, Technology, and Engineering Management, St. Thomas University (STU), Miami Gardens, FL 33054, USA

Special Issue Information

Dear Colleagues,

Complex networks and machine learning methods are useful for the study of complex systems in applied sciences. On one hand, we can use complex networks to represent and study the structure of complex systems in applied sciences. The systems susceptible to study with this approach range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to brain cortex, ecosystems, internet, market, social networks, etc.

On the other hand, computational techniques coming from machine learning (ML) are gaining importance in the analysis of numerical data related to complex systems. Some ML methods include artificial neural networks, support vector machines, etc. In addition, we can use the numerical parameters of complex networks and other input variables to train ML algorithms in order to predict the properties of these systems.

As a result of this reflection, we decided to launch one Special Issue focused on the benefits of using ML and complex network analysis (in combination or separately) to study complex systems in applied sciences. The topic of the issue is: Complex Networks and Machine Learning in Applied Sciences. Accepted papers will be published in the journal Applied Science, which is an open access publication journal of MDPI (https://www.mdpi.com/journal/applsci). The Issue also includes full versions of proceedings published in MOL2NET International Conference Series on Multidisciplinary Sciences, 2017 (closed) and 2018 (open), with official website at SciForum platform. The Sciforum platform is supported by MDPI editorial. Last year (2017), the conference received more than 250 communications from more than 450 authors worldwide. These communications have been presented online and/or in person in more than 10 associated specialized workshops held at universities in the USA, Spain, Portugal, Brazil, etc. MOL2NET 2018 link: http://sciforum.net/conference/mol2net-04.

Prof. Dr. Humbert González-Díaz
Prof. Dr. Maykel Cruz-Monteagudo
Prof. Dr. Terace Fletcher
Prof. Dr. David Quesada
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

complex networks in applied sciences
proteome analysis and protein interaction networks (PINs)
complex networks and systems biology
metabolic pathway networks
brain networks
social, financial, and legal networks
machine learning in applied sciences
machine learning in cheminformatics
machine learning in bioinformatics
machine learning in biomedical engineering
artificial neural networks
support vector machines
systems biology
deep learning

Published Papers (11 papers)

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Editorial

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5 pages, 198 KiB

Open AccessEditorial

Complex Networks and Machine Learning: From Molecular to Social Sciences

by David Quesada, Maykel Cruz-Monteagudo, Terace Fletcher, Aliuska Duardo-Sanchez and Humbert González-Díaz

Appl. Sci. 2019, 9(21), 4493; https://doi.org/10.3390/app9214493 - 23 Oct 2019

Cited by 4 | Viewed by 4190

Abstract

Combining complex networks analysis methods with machine learning (ML) algorithms have become a very useful strategy for the study of complex systems in applied sciences. Noteworthy, the structure and function of such systems can be studied and represented through the above-mentioned approaches, which range from small chemical compounds, proteins, metabolic pathways, and other molecular systems, to neuronal synapsis in the brain’s cortex, ecosystems, the internet, markets, social networks, program’s development in education, social learning, etc. On the other hand, ML algorithms are useful to study large datasets with characteristic features of complex systems. In this context, we decided to launch one special issue focused on the benefits of using ML and complex network analysis (in combination or separately) to study complex systems in applied sciences. The topic of the issue is: Complex Networks and Machine Learning in Applied Sciences. Contributions to this special issue are highlighted below. The present issue is also linked to conference series, MOL2NET International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI AG, SciForum, Basel, Switzerland. At the same time, the special issue and the conference are hosts for the works published by students/tutors of the USEDAT: USA–Europe Data Analysis Training Worldwide Program. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

Research

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27 pages, 5236 KiB

Open AccessArticle

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

by Kamil Bortko, Patryk Pazura, Juho Hamari, Piotr Bartków and Jarosław Jankowski

Appl. Sci. 2019, 9(7), 1268; https://doi.org/10.3390/app9071268 - 27 Mar 2019

Cited by 2 | Viewed by 2588

Abstract

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In ”real” economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods’ circulation can be tested and confirmed before their introduction to ”real life” and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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17 pages, 796 KiB

Open AccessArticle

Regular Equivalence for Social Networks

by Pieter Audenaert, Didier Colle and Mario Pickavet

Appl. Sci. 2019, 9(1), 117; https://doi.org/10.3390/app9010117 - 30 Dec 2018

Cited by 3 | Viewed by 3379

Abstract

Networks and graphs are highly relevant in modeling real-life communities and their interactions. In order to gain insight in their structure, different roles are attributed to vertices, effectively clustering them in equivalence classes. A new formal definition of regular equivalence is presented in this paper, and the relation with other equivalence types is investigated and mathematically proven. An efficient algorithm is designed, able to detect all regularly equivalent roles in large-scale complex networks. We apply it to both Barabási–Albert random networks, as well as real-life social networks, which leads to interesting insights. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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16 pages, 2078 KiB

Open AccessArticle

Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals

by Chen Diao, Bin Wang and Ning Cai

Appl. Sci. 2019, 9(1), 105; https://doi.org/10.3390/app9010105 - 29 Dec 2018

Cited by 5 | Viewed by 3726

Abstract

Twelve-lead Electrocardiograph (ECG) signals fusion is crucial for further ECG signal processing. In this paper, based on the idea of the local weighted linear prediction algorithm, a novel fusion data algorithm is proposed, which was applied in data fusion of the 12-lead ECG signals. In order to analyze the signal quality comprehensively, the quality characteristics should be adequately retained in the final fused result. In our algorithm, the values for the weighted coefficient of state points were closely related to the final fused result. Thus, two fuzzy inference systems were designed to calculate the weighted coefficients. For the sake of assessing the performance of our method, synthetic ECG signals and realistic ECG signals were applied in the experiments. Experimental results indicate that our method can fuse the 12-lead ECG signals effectively with inherit the quality characteristics of original ECG signals inherited properly. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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13 pages, 548 KiB

Open AccessArticle

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

by Virginia Mato-Abad, Isabel Jiménez, Rafael García-Vázquez, José M. Aldrey, Daniel Rivero, Purificación Cacabelos, Javier Andrade-Garda, Juan M. Pías-Peleteiro and Santiago Rodríguez-Yáñez

Appl. Sci. 2018, 8(9), 1629; https://doi.org/10.3390/app8091629 - 12 Sep 2018

Cited by 4 | Viewed by 3501

Abstract

Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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15 pages, 4307 KiB

Open AccessArticle

Multi-View Object Detection Based on Deep Learning

by Cong Tang, Yongshun Ling, Xing Yang, Wei Jin and Chao Zheng

Appl. Sci. 2018, 8(9), 1423; https://doi.org/10.3390/app8091423 - 21 Aug 2018

Cited by 32 | Viewed by 7641

Abstract

A multi-view object detection approach based on deep learning is proposed in this paper. Classical object detection methods based on regression models are introduced, and the reasons for their weak ability to detect small objects are analyzed. To improve the performance of these methods, a multi-view object detection approach is proposed, and the model structure and working principles of this approach are explained. Additionally, the object retrieval ability and object detection accuracy of both the multi-view methods and the corresponding classical methods are evaluated and compared based on a test on a small object dataset. The experimental results show that in terms of object retrieval capability, Multi-view YOLO (You Only Look Once: Unified, Real-Time Object Detection), Multi-view YOLOv2 (based on an updated version of YOLO), and Multi-view SSD (Single Shot Multibox Detector) achieve AF (average F-measure) scores that are higher than those of their classical counterparts by 0.177, 0.06, and 0.169, respectively. Moreover, in terms of the detection accuracy, when difficult objects are not included, the mAP (mean average precision) scores of the multi-view methods are higher than those of the classical methods by 14.3%, 7.4%, and 13.1%, respectively. Thus, the validity of the approach proposed in this paper has been verified. In addition, compared with state-of-the-art methods based on region proposals, multi-view detection methods are faster while achieving mAPs that are approximately the same in small object detection. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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28 pages, 3785 KiB

Open AccessArticle

Node-Based Resilience Measure Clustering with Applications to Noisy and Overlapping Communities in Complex Networks

by John Matta, Tayo Obafemi-Ajayi, Jeffrey Borwey, Koushik Sinha, Donald Wunsch and Gunes Ercal

Appl. Sci. 2018, 8(8), 1307; https://doi.org/10.3390/app8081307 - 06 Aug 2018

Cited by 13 | Viewed by 3811

Abstract

This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we experimentally validate the usefulness of such methods in accomplishing the following: (i) clustering a graph in one step without knowing the number of clusters a priori; (ii) removing noise from noisy data; and (iii) detecting overlapping communities. We demonstrate that this clustering schema can be applied successfully using a wide range of data, including both real and synthetic networks, both natively in graph form and also expressed as point sets. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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11 pages, 1195 KiB

Open AccessArticle

Roadmap Modeling and Assessment Approach for Defense Technology System of Systems

by Hui Lu and Hanlin You

Appl. Sci. 2018, 8(6), 908; https://doi.org/10.3390/app8060908 - 01 Jun 2018

Cited by 12 | Viewed by 4282

Abstract

Advanced defense technology plays a crucial role in safeguarding national safety and development interests. Aiming to handle the problems of current research and development (R&D) management approaches faced with the rocketing complexities of system of systems, the authors propose a novel roadmap modeling and assessment methodology through studying the driving forces of general technology development and analyzing realistic requirements of defense technology management in this article. First, a requirement decomposition framework is designed based on multi-view theories and text-mining tools are used to construct a multi-layer knowledge-flow network model. Second, the contribution rates of requirement elements at different levels are evaluated using a multi-criteria decision-making approach and the node importance is assessed based on the topological structure of multi-layer network. Third, it is utilized to demonstrate the effectiveness of the proposed approaches that illustrative examples of the technology requirements in maritime security strategy investigating and a dual-layer knowledge-flow network consists of patents that belong to the “Coherent Light Generator (CLC)” classification from the United States Patent and Trademark Office (USPTO) database and the related academic papers from Web of Science. Finally, the contributions, potential applications, and drawbacks of this work are discussed and research outlooks are provided. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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11 pages, 5435 KiB

Open AccessArticle

Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

by Yingchun Chen, Ou Li, Yu Sun and Fei Li

Appl. Sci. 2018, 8(4), 620; https://doi.org/10.3390/app8040620 - 16 Apr 2018

Cited by 5 | Viewed by 3065

Abstract

With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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Other

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19 pages, 1247 KiB

Open AccessConference Report

Modeling Properties with Artificial Neural Networks and Multilinear Least-Squares Regression: Advantages and Drawbacks of the Two Methods

by Jesus Vicente De Julián-Ortiz, Lionello Pogliani and Emili Besalú

Appl. Sci. 2018, 8(7), 1094; https://doi.org/10.3390/app8071094 - 05 Jul 2018

Cited by 8 | Viewed by 2890

Abstract

The mean molecular connectivity indices (MMCI) proposed in previous studies are used in conjunction with well-known molecular connectivity indices (MCI) to model eleven properties of organic solvents. The MMCI and MCI descriptors selected by the stepwise multilinear least-squares (MLS) procedure were used to perform artificial neural network (ANN) computations, with the aim of detecting the advantages and limits of the ANN approach. The MLS procedure can replicate the obtained results for as long as is needed, a characteristic not shared by the ANN methodology, which, on the one hand increases the quality of a description, and on the other hand also results in overfitting. The present study also reveals how ANN methods prefer MCI relatively to MMCI descriptors. Four types of ANN computations show that: (i) MMCI descriptors are preferred with properties with a small number of points, (ii) MLS is preferred over ANN when the number of ANN weights is similar to the number of regression coefficients and, (iii) in some cases, the MLS modeling quality is similar to the modeling quality of ANN computations. Both the common training set and an external randomly chosen validation set were used throughout the paper. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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11 pages, 1346 KiB

Open AccessEditor’s ChoiceLetter

Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN

by Yun Ren, Changren Zhu and Shunping Xiao

Appl. Sci. 2018, 8(5), 813; https://doi.org/10.3390/app8050813 - 18 May 2018

Cited by 210 | Viewed by 13096

Abstract

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects. Full article

(This article belongs to the Special Issue Complex Networks and Machine Learning: From Molecular to Social Sciences)

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