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Computation in Complex Networks
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Computation in Complex Networks

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 62153

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Clara Pizzuti

E-Mail Website
Guest Editor
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: evolutionary computation; complex network analysis and mining; data mining; data streams
Special Issues, Collections and Topics in MDPI journals
Dr. Annalisa Socievole

E-Mail
Guest Editor
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: complex networks; community detection; evolutionary computation; network robustness

Special Issue Information

Dear Colleagues,

Complex networks, in recent years, are increasingly attracting the attention of researchers from many different domains, such as physics, mathematics, biology, medicine, engineering, and computer science, among others. Complex networks are able to model a wide variety of structures that support the functioning of daily life, including high technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. Understanding how complex systems behave is thus an imperative for many different fields due to their interwoven and multidisciplinary nature and inherent complexity.

This Special Issue aims at collecting original and high-quality papers within the research field of complex network computation. When investigating complex systems, several relevant questions arise such as how information/viruses spread, how groups of nodes/diseases form and evolve, and how to detect and improve robustness over their non-trivial topological structure, just to provide some examples. Papers analyzing transportation infrastructures, communication networks, financial networks, political networks, power grid systems, ecosystems, bioinformatics and network medicine aspects are welcome. We also invite papers conceptualizing complex systems through theoretical frameworks.

Prof. Dr. Clara Pizzuti
Dr. Annalisa Socievole
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

  • Community discovery in complex networks
  • Motif discovery in complex networks
  • Link prediction in complex networks
  • Anomaly detection in complex networks
  • Complex networks modeling and analysis
  • Multilayer, temporal and heterogeneous networks
  • Information spreading in complex networks
  • Social influence, reputation and trust in complex networks
  • Visual representation of complex networks
  • Epidemics in complex networks
  • Cascading failures in complex networks
  • Attack vulnerability, resilience and robustness in complex networks
  • Political networks
  • Financial networks
  • Complex networks for IoT, smart cities and smart grids
  • Network medicine
  • Mobile complex networks

Published Papers (21 papers)

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Editorial

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5 pages, 188 KiB  
Editorial
Computation in Complex Networks
by Clara Pizzuti and Annalisa Socievole
Entropy 2021, 23(2), 192; https://0-doi-org.brum.beds.ac.uk/10.3390/e23020192 - 05 Feb 2021
Cited by 1 | Viewed by 1312
Abstract
The Special Issue on “Computation in Complex Networks” focused on gathering highly original papers in the field of current complex network research [...] Full article
(This article belongs to the Special Issue Computation in Complex Networks)

Research

Jump to: Editorial

20 pages, 696 KiB  
Article
Active Learning for Node Classification: An Evaluation
by Kaushalya Madhawa and Tsuyoshi Murata
Entropy 2020, 22(10), 1164; https://0-doi-org.brum.beds.ac.uk/10.3390/e22101164 - 16 Oct 2020
Cited by 19 | Viewed by 3490
Abstract
Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being used as a [...] Read more.
Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being used as a solution to train classification models with less labeled instances by selecting only the most informative instances for labeling. This is especially important when the labeled data are scarce or the labeling process is expensive. In this paper, we study the application of active learning on attributed graphs. In this setting, the data instances are represented as nodes of an attributed graph. Graph neural networks achieve the current state-of-the-art classification performance on attributed graphs. The performance of graph neural networks relies on the careful tuning of their hyperparameters, usually performed using a validation set, an additional set of labeled instances. In label scarce problems, it is realistic to use all labeled instances for training the model. In this setting, we perform a fair comparison of the existing active learning algorithms proposed for graph neural networks as well as other data types such as images and text. With empirical results, we demonstrate that state-of-the-art active learning algorithms designed for other data types do not perform well on graph-structured data. We study the problem within the framework of the exploration-vs.-exploitation trade-off and propose a new count-based exploration term. With empirical evidence on multiple benchmark graphs, we highlight the importance of complementing uncertainty-based active learning models with an exploration term. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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16 pages, 355 KiB  
Article
Spreading Control in Two-Layer Multiplex Networks
by Roberto Bernal Jaquez, Luis Angel Alarcón Ramos and Alexander Schaum
Entropy 2020, 22(10), 1157; https://0-doi-org.brum.beds.ac.uk/10.3390/e22101157 - 15 Oct 2020
Cited by 4 | Viewed by 1797
Abstract
The problem of controlling a spreading process in a two-layer multiplex networks in such a way that the extinction state becomes a global attractor is addressed. The problem is formulated in terms of a Markov-chain based susceptible-infected-susceptible (SIS) dynamics in a complex multilayer [...] Read more.
The problem of controlling a spreading process in a two-layer multiplex networks in such a way that the extinction state becomes a global attractor is addressed. The problem is formulated in terms of a Markov-chain based susceptible-infected-susceptible (SIS) dynamics in a complex multilayer network. The stabilization of the extinction state for the nonlinear discrete-time model by means of appropriate adaptation of system parameters like transition rates within layers and between layers is analyzed using a dominant linear dynamics yielding global stability results. An answer is provided for the central question about the essential changes in the step from a single to a multilayer network with respect to stability criteria and the number of nodes that need to be controlled. The results derived rigorously using mathematical analysis are verified using statical evaluations about the number of nodes to be controlled and by simulation studies that illustrate the stability property of the multilayer network induced by appropriate control action. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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14 pages, 1704 KiB  
Article
Investigating the Influence of Inverse Preferential Attachment on Network Development
by Cynthia S. Q. Siew and Michael S. Vitevitch
Entropy 2020, 22(9), 1029; https://0-doi-org.brum.beds.ac.uk/10.3390/e22091029 - 15 Sep 2020
Cited by 11 | Viewed by 2654
Abstract
Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing [...] Read more.
Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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13 pages, 1392 KiB  
Article
Classification of Literary Works: Fractality and Complexity of the Narrative, Essay, and Research Article
by Aldo Ramirez-Arellano
Entropy 2020, 22(8), 904; https://0-doi-org.brum.beds.ac.uk/10.3390/e22080904 - 17 Aug 2020
Cited by 6 | Viewed by 2450
Abstract
A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, [...] Read more.
A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, betweenness, shortest path length, clustering coefficient, and nearest neighbourhoods’ degree, as well as more complex measures such as: the fractal dimension, the complexity of a given network, the Area Under Box-covering, and the Area Under the Robustness Curve. The literary works of Mexican writers were classify according to their genre. Precisely 87% of the full word co-occurrence networks were classified as a fractal. Also, empirical evidence is presented that supports the conjecture that lemmatisation of the original text is a renormalisation process of the networks that preserve their fractal property and reveal stylistic attributes by genre. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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19 pages, 5330 KiB  
Article
Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices
by Chen-Kun Tsung, Hann-Jang Ho, Chien-Yu Chen, Tien-Wei Chang and Sing-Ling Lee
Entropy 2020, 22(8), 819; https://0-doi-org.brum.beds.ac.uk/10.3390/e22080819 - 27 Jul 2020
Cited by 6 | Viewed by 2375
Abstract
On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult [...] Read more.
On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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33 pages, 1286 KiB  
Article
Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations
by Alessio Martino, Enrico De Santis, Alessandro Giuliani and Antonello Rizzi
Entropy 2020, 22(7), 794; https://0-doi-org.brum.beds.ac.uk/10.3390/e22070794 - 21 Jul 2020
Cited by 7 | Viewed by 3422
Abstract
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of [...] Read more.
Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins’ functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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16 pages, 940 KiB  
Article
Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping
by Yongpeng Wang, Hong Yu, Guoyin Wang and Yongfang Xie
Entropy 2020, 22(4), 473; https://0-doi-org.brum.beds.ac.uk/10.3390/e22040473 - 20 Apr 2020
Cited by 10 | Viewed by 3703
Abstract
Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label [...] Read more.
Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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18 pages, 578 KiB  
Article
Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow
by Tyson Pond, Saranzaya Magsarjav, Tobin South, Lewis Mitchell and James P. Bagrow
Entropy 2020, 22(3), 265; https://0-doi-org.brum.beds.ac.uk/10.3390/e22030265 - 26 Feb 2020
Cited by 11 | Viewed by 4204
Abstract
Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of [...] Read more.
Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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21 pages, 715 KiB  
Article
Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization
by Jun Jin Choong, Xin Liu and Tsuyoshi Murata
Entropy 2020, 22(2), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020197 - 07 Feb 2020
Cited by 14 | Viewed by 5493
Abstract
Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based [...] Read more.
Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based approaches. Specifically, merely minimizing the loss of VAE increases the deviation from its primary objective. Focusing on Variational Graph Autoencoder for Community Detection (VGAECD) we found that optimizing the loss using the stochastic gradient descent often leads to sub-optimal community structure especially when initialized poorly. We address this shortcoming by introducing a dual optimization procedure. This procedure aims to guide the optimization process and encourage learning of the primary objective. Additionally, we linearize the encoder to reduce the number of learning parameters. The outcome is a robust algorithm that outperforms its predecessor. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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15 pages, 574 KiB  
Article
Properties of the Vascular Networks in Malignant Tumors
by Juan Carlos Chimal-Eguía, Erandi Castillo-Montiel and Ricardo T. Paez-Hernández
Entropy 2020, 22(2), 166; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020166 - 31 Jan 2020
Cited by 7 | Viewed by 1909
Abstract
This work presents an analysis for real and synthetic angiogenic networks using a tomography image that obtains a portrait of a vascular network. After the image conversion into a binary format it is possible to measure various network properties, which includes the average [...] Read more.
This work presents an analysis for real and synthetic angiogenic networks using a tomography image that obtains a portrait of a vascular network. After the image conversion into a binary format it is possible to measure various network properties, which includes the average path length, the clustering coefficient, the degree distribution and the fractal dimension. When comparing the observed properties with that produced by the Invasion Percolation algorithm (IPA), we observe that there exist differences between the properties obtained by the real and the synthetic networks produced by the IPA algorithm. Taking into account the former, a new algorithm which models the expansion of an angiogenic network through randomly heuristic rules is proposed. When comparing this new algorithm with the real networks it is observed that now both share some properties. Once creating synthetic networks, we prove the robustness of the network by subjecting the original angiogenic and the synthetic networks to the removal of the most connected nodes, and see to what extent the properties changed. Using this concept of robustness, in a very naive fashion it is possible to launch a hypothetical proposal for a therapeutic treatment based on the robustness of the network. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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8 pages, 3188 KiB  
Communication
Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy
by Jiancheng Sun
Entropy 2020, 22(2), 142; https://0-doi-org.brum.beds.ac.uk/10.3390/e22020142 - 24 Jan 2020
Cited by 4 | Viewed by 2315
Abstract
The analysis of chaotic time series is usually a challenging task due to its complexity. In this communication, a method of complex network construction is proposed for univariate chaotic time series, which provides a novel way to analyze time series. In the process [...] Read more.
The analysis of chaotic time series is usually a challenging task due to its complexity. In this communication, a method of complex network construction is proposed for univariate chaotic time series, which provides a novel way to analyze time series. In the process of complex network construction, how to measure the similarity between the time series is a key problem to be solved. Due to the complexity of chaotic systems, the common metrics is hard to measure the similarity. Consequently, the proposed method first transforms univariate time series into high-dimensional phase space to increase its information, then uses Gaussian mixture model (GMM) to represent time series, and finally introduces maximum mean discrepancy (MMD) to measure the similarity between GMMs. The Lorenz system is used to validate the correctness and effectiveness of the proposed method for measuring the similarity. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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12 pages, 2083 KiB  
Article
Analyzing Uncertainty in Complex Socio-Ecological Networks
by Ana D. Maldonado, María Morales, Pedro A. Aguilera and Antonio Salmerón
Entropy 2020, 22(1), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/e22010123 - 19 Jan 2020
Cited by 11 | Viewed by 2954
Abstract
Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains [...] Read more.
Socio-ecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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16 pages, 1015 KiB  
Article
Multi-Type Node Detection in Network Communities
by Chinenye Ezeh, Ren Tao, Li Zhe, Wang Yiqun and Qu Ying
Entropy 2019, 21(12), 1237; https://0-doi-org.brum.beds.ac.uk/10.3390/e21121237 - 17 Dec 2019
Cited by 7 | Viewed by 3296
Abstract
Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types [...] Read more.
Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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22 pages, 5972 KiB  
Article
Predicting the Evolution of Physics Research from a Complex Network Perspective
by Wenyuan Liu, Stanisław Saganowski, Przemysław Kazienko and Siew Ann Cheong
Entropy 2019, 21(12), 1152; https://0-doi-org.brum.beds.ac.uk/10.3390/e21121152 - 26 Nov 2019
Cited by 5 | Viewed by 2936
Abstract
The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that [...] Read more.
The advancement of science, as outlined by Popper and Kuhn, is largely qualitative, but with bibliometric data, it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore, it is also important to allocate finite resources to research topics that have the growth potential to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analyzing the APS data sets from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging, and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increased significantly for merging events and decreased significantly for splitting events. Our results represent the first step from a descriptive understanding of the science of science (SciSci), towards one that is ultimately prescriptive. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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14 pages, 485 KiB  
Article
Uncovering the Dependence of Cascading Failures on Network Topology by Constructing Null Models
by Lin Ding, Si-Yuan Liu, Quan Yang and Xiao-Ke Xu
Entropy 2019, 21(11), 1119; https://0-doi-org.brum.beds.ac.uk/10.3390/e21111119 - 15 Nov 2019
Cited by 7 | Viewed by 2410
Abstract
Cascading failures are the significant cause of network breakdowns in a variety of complex infrastructure systems. Given such a system, uncovering the dependence of cascading failures on its underlying topology is essential but still not well explored in the field of complex networks. [...] Read more.
Cascading failures are the significant cause of network breakdowns in a variety of complex infrastructure systems. Given such a system, uncovering the dependence of cascading failures on its underlying topology is essential but still not well explored in the field of complex networks. This study offers an original approach to systematically investigate the association between cascading failures and topological variation occurring in realistic complex networks by constructing different types of null models. As an example of its application, we study several standard Internet networks in detail. The null models first transform the original network into a series of randomized networks representing alternate realistic topologies, while taking its basic topological characteristics into account. Then considering the routing rule of shortest-path flow, it is sought to determine the implications of different topological circumstances, and the findings reveal the effects of micro-scale (such as degree distribution, assortativity, and transitivity) and meso-scale (such as rich-club and community structure) features on the cascade damage caused by deliberate node attacks. Our results demonstrate that the proposed method is suitable and promising to comprehensively analyze realistic influence of various topological properties, providing insight into designing the networks to make them more robust against cascading failures. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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18 pages, 4863 KiB  
Article
Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture
by Siqi Xiong, Feng Zhu, Yiping Yao, Wenjie Tang and Yuhao Xiao
Entropy 2019, 21(9), 891; https://0-doi-org.brum.beds.ac.uk/10.3390/e21090891 - 14 Sep 2019
Cited by 6 | Viewed by 2550
Abstract
With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to [...] Read more.
With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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12 pages, 1253 KiB  
Article
Minimum Memory-Based Sign Adjustment in Signed Social Networks
by Mingze Qi, Hongzhong Deng and Yong Li
Entropy 2019, 21(8), 728; https://0-doi-org.brum.beds.ac.uk/10.3390/e21080728 - 25 Jul 2019
Cited by 2 | Viewed by 2225
Abstract
In social networks comprised of positive (P) and negative (N) symmetric relations, individuals (nodes) will, under the stress of structural balance, alter their relations (links or edges) with their neighbours, either from positive to negative or vice versa. In the real world, individuals [...] Read more.
In social networks comprised of positive (P) and negative (N) symmetric relations, individuals (nodes) will, under the stress of structural balance, alter their relations (links or edges) with their neighbours, either from positive to negative or vice versa. In the real world, individuals can only observe the influence of their adjustments upon the local balance of the network and take this into account when adjusting their relationships. Sometime, their local adjustments may only respond to their immediate neighbourhoods, or centre upon the most important neighbour. To study whether limited memory affects the convergence of signed social networks, we introduce a signed social network model, propose random and minimum memory-based sign adjustment rules, and analyze and compare the impacts of an initial ratio of positive links, rewire probability, network size, neighbor number, and randomness upon structural balance under these rules. The results show that, with an increase of the rewiring probability of the generated network and neighbour number, it is more likely for the networks to globally balance under the minimum memory-based adjustment. While the Newmann-Watts small world model (NW) network becomes dense, the counter-intuitive phenomena emerges that the network will be driven to a global balance, even under the minimum memory-based local sign adjustment, no matter the network size and initial ratio of positive links. This can help to manage and control huge networks with imited resources. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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15 pages, 916 KiB  
Article
A SOM-Based Membrane Optimization Algorithm for Community Detection
by Chuang Liu, Yingkui Du and Jiahao Lei
Entropy 2019, 21(5), 533; https://0-doi-org.brum.beds.ac.uk/10.3390/e21050533 - 25 May 2019
Cited by 7 | Viewed by 3306
Abstract
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. [...] Read more.
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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12 pages, 2896 KiB  
Article
Image Entropy for the Identification of Chimera States of Spatiotemporal Divergence in Complex Coupled Maps of Matrices
by Rasa Smidtaite, Guangqing Lu and Minvydas Ragulskis
Entropy 2019, 21(5), 523; https://0-doi-org.brum.beds.ac.uk/10.3390/e21050523 - 24 May 2019
Cited by 5 | Viewed by 3474
Abstract
Complex networks of coupled maps of matrices (NCMM) are investigated in this paper. It is shown that a NCMM can evolve into two different steady states—the quiet state or the state of divergence. It appears that chimera states of spatiotemporal divergence do exist [...] Read more.
Complex networks of coupled maps of matrices (NCMM) are investigated in this paper. It is shown that a NCMM can evolve into two different steady states—the quiet state or the state of divergence. It appears that chimera states of spatiotemporal divergence do exist in the regions around the boundary lines separating these two steady states. It is demonstrated that digital image entropy can be used as an effective measure for the visualization of these regions of chimera states in different networks (regular, feed-forward, random, and small-world NCMM). Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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17 pages, 1150 KiB  
Article
Evolution Model of Spatial Interaction Network in Online Social Networking Services
by Jian Dong, Bin Chen, Pengfei Zhang, Chuan Ai, Fang Zhang, Danhuai Guo and Xiaogang Qiu
Entropy 2019, 21(4), 434; https://0-doi-org.brum.beds.ac.uk/10.3390/e21040434 - 24 Apr 2019
Cited by 2 | Viewed by 2873
Abstract
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, [...] Read more.
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
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