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Complexity, Entropy and the Physics of Information
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Complexity, Entropy and the Physics of Information

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 24004

Special Issue Editors

Prof. Dr. Yi-Cheng Zhang

E-Mail Website
Guest Editor
Department of Physics, University of Fribourg, CH-1700 Fribourg, Switzerland
Interests: statistical physics; information networks; information economy; complex network
Special Issues, Collections and Topics in MDPI journals
Dr. Shimin Cai

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: complexity science; time series analysis; complex network; data mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex network research now involves a large community of interdisciplinary scholars of computer scientists, mathematicians, economists, and especially physicists. This has resulted in a better understanding of complex phenomena—from the work of Barabasi and Albert to the collaboration networks of Newman. In fact, complexity science as a whole has been gaining traction among physicists, and it has found great application within the framework of statistical mechanics, such as in the example of the belief propagation algorithm. Moreover, the rise of a more connected world, such as through the internet, has given rise to many more possibilities to study such complex structures and to model the inner mechanisms backed up by real world data. This makes complexity theory a fertile ground to work on, where one can develop new methods and test new ideas to solve the various problems in complexity and network studies.

This Special Issue focuses on recent advances in complex networks and their applications in computer science, physics, biomedicine, and more.

Prof. Dr. Yi-Cheng Zhang
Dr. Shimin Cai
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

  • complex networks
  • complexity theory
  • influence propagation
  • diffusion model
  • community structure

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Published Papers (15 papers)

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Research

11 pages, 1626 KiB  
Article
Evolution Dynamics Model of Private Enterprises under Simultaneous and Sequential Innovation Decisions
by Chi Zhang, Yutong Wang and Tingqiang Chen
Entropy 2023, 25(11), 1553; https://doi.org/10.3390/e25111553 - 17 Nov 2023
Viewed by 663
Abstract
The innovation of private enterprises plays a crucial role. This study focuses on the impacts of market information asymmetry, the technology spillover effect, and the order of innovation research and development (R&D) decisions on the evolution of private enterprises’ innovation. This study constructs [...] Read more.
The innovation of private enterprises plays a crucial role. This study focuses on the impacts of market information asymmetry, the technology spillover effect, and the order of innovation research and development (R&D) decisions on the evolution of private enterprises’ innovation. This study constructs a dynamic model to analyze how the innovation decision-making order of private enterprises influences their profits and intertemporal innovation decision making. First, we derive the equilibrium point under sequential decisions and the stability of the system at the equilibrium point. Second, we investigate the impact of sequential and simultaneous innovation decisions on the evolution of the dynamic system and its economic implications. Finally, we study the evolutionary dynamics of the attractor with the rate of innovation adjustment and point to the existence of multiple equilibria. The results suggest that the speed of the innovation R&D cost change should be moderate, and the asynchronous updating of the innovation R&D strategy can prevent the system evolution from turning into chaos. These conclusions guide innovation policies. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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12 pages, 378 KiB  
Article
Evolution of Robustness in Growing Random Networks
by Melvyn Tyloo
Entropy 2023, 25(9), 1340; https://doi.org/10.3390/e25091340 - 15 Sep 2023
Viewed by 823
Abstract
Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges [...] Read more.
Networks are widely used to model the interaction between individual dynamic systems. In many instances, the total number of units and interaction coupling are not fixed in time, and instead constantly evolve. In networks, this means that the number of nodes and edges both change over time. Various properties of coupled dynamic systems, such as their robustness against noise, essentially depend on the structure of the interaction network. Therefore, it is of considerable interest to predict how these properties are affected when the network grows as well as their relationship to the growth mechanism. Here, we focus on the time evolution of a network’s Kirchhoff index. We derive closed-form expressions for its variation in various scenarios, including the addition of both edges and nodes. For the latter case, we investigate the evolution where single nodes with one or two edges connecting to existing nodes are added recursively to a network. In both cases, we derive the relations between the properties of the nodes to which the new node connects along with the global evolution of network robustness. In particular, we show how different scalings of the Kirchhoff index can be obtained as a function of the number of nodes. We illustrate and confirm this theory via numerical simulations of randomly growing networks. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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13 pages, 980 KiB  
Article
Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks
by Wenyan Peng, Tingting Chen, Bo Zheng and Xiongfei Jiang
Entropy 2023, 25(8), 1240; https://doi.org/10.3390/e25081240 - 21 Aug 2023
Cited by 1 | Viewed by 1094
Abstract
The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and [...] Read more.
The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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14 pages, 2416 KiB  
Article
Identifying Important Nodes in Trip Networks and Investigating Their Determinants
by Ze-Tao Li, Wei-Peng Nie, Shi-Min Cai, Zhi-Dan Zhao and Tao Zhou
Entropy 2023, 25(6), 958; https://doi.org/10.3390/e25060958 - 20 Jun 2023
Cited by 1 | Viewed by 1314
Abstract
Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability [...] Read more.
Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu’s trip networks, while no such phenomenon is evident in New York City’s. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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14 pages, 744 KiB  
Article
Coupled Information–Epidemic Spreading Dynamics with Selective Mass Media
by Jiajun Xian, Zhihong Zhang, Zongyi Li and Dan Yang
Entropy 2023, 25(6), 927; https://doi.org/10.3390/e25060927 - 12 Jun 2023
Viewed by 1214
Abstract
As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information–epidemic dynamics, while [...] Read more.
As a pandemic emerges, information on epidemic prevention disseminates among the populace, and the propagation of that information interacts with the proliferation of the disease. Mass media serve a pivotal function in facilitating the dissemination of epidemic-related information. Investigating coupled information–epidemic dynamics, while accounting for the promotional effect of mass media in information dissemination, is of significant practical relevance. Nonetheless, in the extant research, scholars predominantly employ an assumption that mass media broadcast to all individuals equally within the network: this assumption overlooks the practical constraint imposed by the substantial social resources required to accomplish such comprehensive promotion. In response, this study introduces a coupled information–epidemic spreading model with mass media that can selectively target and disseminate information to a specific proportion of high-degree nodes. We employed a microscopic Markov chain methodology to scrutinize our model, and we examined the influence of the various model parameters on the dynamic process. The findings of this study reveal that mass media broadcasts directed towards high-degree nodes within the information spreading layer can substantially reduce the infection density of the epidemic, and raise the spreading threshold of the epidemic. Additionally, as the mass media broadcast proportion increases, the suppression effect on the disease becomes stronger. Moreover, with a constant broadcast proportion, the suppression effect of mass media promotion on epidemic spreading within the model is more pronounced in a multiplex network with a negative interlayer degree correlation, compared to scenarios with positive or absent interlayer degree correlation. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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16 pages, 1419 KiB  
Article
Predicting the Popularity of Information on Social Platforms without Underlying Network Structure
by Leilei Wu, Lingling Yi, Xiao-Long Ren and Linyuan Lü
Entropy 2023, 25(6), 916; https://doi.org/10.3390/e25060916 - 9 Jun 2023
Viewed by 1644
Abstract
The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or [...] Read more.
The ability to predict the size of information cascades in online social networks is crucial for various applications, including decision-making and viral marketing. However, traditional methods either rely on complicated time-varying features that are challenging to extract from multilingual and cross-platform content, or on network structures and properties that are often difficult to obtain. To address these issues, we conducted empirical research using data from two well-known social networking platforms, WeChat and Weibo. Our findings suggest that the information-cascading process is best described as an activate–decay dynamic process. Building on these insights, we developed an activate–decay (AD)-based algorithm that can accurately predict the long-term popularity of online content based solely on its early repost amount. We tested our algorithm using data from WeChat and Weibo, demonstrating that we could fit the evolution trend of content propagation and predict the longer-term dynamics of message forwarding from earlier data. We also discovered a close correlation between the peak forwarding amount of information and the total amount of dissemination. Finding the peak of the amount of information dissemination can significantly improve the prediction accuracy of our model. Our method also outperformed existing baseline methods for predicting the popularity of information. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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13 pages, 4528 KiB  
Article
Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency
by Mengtian Cui, Kai Li, Yulan Li, Dany Kamuhanda and Claudio J. Tessone
Entropy 2023, 25(4), 681; https://doi.org/10.3390/e25040681 - 19 Apr 2023
Cited by 3 | Viewed by 1592
Abstract
Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time [...] Read more.
Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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15 pages, 2796 KiB  
Article
Analysis of Influence of Behavioral Adoption Threshold Diversity on Multi-Layer Network
by Gang Deng, Yuting Peng, Yang Tian and Xuzhen Zhu
Entropy 2023, 25(3), 458; https://doi.org/10.3390/e25030458 - 6 Mar 2023
Cited by 2 | Viewed by 1062
Abstract
The same people exhibit various adoption behaviors for the same information on various networks. Previous studies, however, did not examine the variety of adoption behaviors on multi-layer networks or take into consideration this phenomenon. Therefore, we refer to this phenomenon, which lacks systematic [...] Read more.
The same people exhibit various adoption behaviors for the same information on various networks. Previous studies, however, did not examine the variety of adoption behaviors on multi-layer networks or take into consideration this phenomenon. Therefore, we refer to this phenomenon, which lacks systematic analysis and investigation, as behavioral adoption diversity on multi-layered networks. Meanwhile, individual adoption behaviors have LTI (local trend imitation) characteristics that help spread information. In order to study the diverse LTI behaviors on information propagation, a two-layer network model is presented. Following that, we provide two adoption threshold functions to describe diverse LTI behaviors. The crossover phenomena in the phase transition is shown to exist through theoretical derivation and experimental simulation. Specifically, the final spreading scale displays a second-order continuous phase transition when individuals exhibit active LTI behaviors, and, when individuals behave negatively, a first-order discontinuous phase transition can be noticed in the final spreading scale. Additionally, the propagation phenomena might be impacted by the degree distribution heterogeneity. Finally, there is a good agreement between the outcomes of our theoretical analysis and simulation. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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12 pages, 3158 KiB  
Article
The Absence of a Weak-Tie Effect When Predicting Large-Weight Links in Complex Networks
by Chengjun Zhang, Qi Li, Yi Lei, Ming Qian, Xinyu Shen, Di Cheng and Wenbin Yu
Entropy 2023, 25(3), 422; https://doi.org/10.3390/e25030422 - 26 Feb 2023
Cited by 1 | Viewed by 1252
Abstract
Link prediction is a hot issue in information filtering. Link prediction algorithms, based on local similarity indices, are widely used in many fields due to their high efficiency and high prediction accuracy. However, most existing link prediction algorithms are available for unweighted networks, [...] Read more.
Link prediction is a hot issue in information filtering. Link prediction algorithms, based on local similarity indices, are widely used in many fields due to their high efficiency and high prediction accuracy. However, most existing link prediction algorithms are available for unweighted networks, and there are relatively few studies for weighted networks. In the previous studies on weighted networks, some scholars pointed out that links with small weights play a more important role in link prediction and emphasized that weak-ties theory has a significant impact on prediction accuracy. On this basis, we studied the edges with different weights, and we discovered that, for edges with large weights, this weak-ties theory actually does not work; Instead, the weak-ties theory works in the prediction of edges with small weights. Our discovery has instructive implications for link predictions in weighted networks. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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12 pages, 1627 KiB  
Article
Behavioral Propagation Based on Passionate Psychology on Single Networks with Limited Contact
by Siyuan Liu, Yang Tian and Xuzhen Zhu
Entropy 2023, 25(2), 303; https://doi.org/10.3390/e25020303 - 6 Feb 2023
Cited by 2 | Viewed by 1322
Abstract
Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this [...] Read more.
Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this paper, we study the influence of sensitive behavior and the heterogeneity of individual contact ability in a single-layer limited-contact network, and propose a single-layer model with limited contact that includes passionate psychology behaviors. Then, a generalized edge partition theory is used to study the information propagation mechanism of the model. Experimental results show that a cross-phase transition occurs. In this model, when individuals display positive passionate psychology behaviors, the final spreading scope will show a second-order continuous increase. When the individual exhibits negative sensitive behavior, the final spreading scope will show a first-order discontinuous increase In addition, heterogeneity in individuals’ limited contact capabilities alters the speed of information propagation and the pattern of global adoption. Eventually, the outcomes of the theoretic analysis match those of the simulations. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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22 pages, 1069 KiB  
Article
A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks
by Khushnood Abbas, Alireza Abbasi, Shi Dong, Ling Niu, Liyong Chen and Bolun Chen
Entropy 2023, 25(2), 257; https://doi.org/10.3390/e25020257 - 31 Jan 2023
Cited by 3 | Viewed by 2470
Abstract
Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to [...] Read more.
Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given’s angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein–protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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11 pages, 1348 KiB  
Article
Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks
by Chengjun Zhang, Yi Lei, Xinyu Shen, Qi Li, Hui Yao, Di Cheng, Yifan Xie and Wenbin Yu
Entropy 2023, 25(1), 22; https://doi.org/10.3390/e25010022 - 23 Dec 2022
Cited by 3 | Viewed by 1774
Abstract
The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order [...] Read more.
The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network’s giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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17 pages, 1987 KiB  
Article
An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality
by Wenxin Jiang, Guochang Zhu, Yiyun Shen, Qian Xie, Min Ji and Yongtao Yu
Entropy 2022, 24(12), 1803; https://doi.org/10.3390/e24121803 - 9 Dec 2022
Cited by 6 | Viewed by 1361
Abstract
Air quality has a significant influence on people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants [...] Read more.
Air quality has a significant influence on people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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13 pages, 3293 KiB  
Article
Personalized Sliding Window Recommendation Algorithm Based on Sequence Alignment
by Lei Zhou, Bolun Chen, Hu Liu and Liuyang Wang
Entropy 2022, 24(11), 1662; https://doi.org/10.3390/e24111662 - 15 Nov 2022
Viewed by 1475
Abstract
With the explosive growth of the amount of information in social networks, the recommendation system, as an application of social networks, has attracted widespread attention in recent years on how to obtain user-interested content in massive data. At present, in the process of [...] Read more.
With the explosive growth of the amount of information in social networks, the recommendation system, as an application of social networks, has attracted widespread attention in recent years on how to obtain user-interested content in massive data. At present, in the process of algorithm design of the recommending system, most methods ignore structural relationships between users. Therefore, in this paper, we designed a personalized sliding window for different users by combining timing information and network topology information, then extracted the information sequence of each user in the sliding window and obtained the similarity between users through sequence alignment. The algorithm only needs to extract part of the data in the original dataset, and the time series comparison shows that our method is superior to the traditional algorithm in recommendation Accuracy, Popularity, and Diversity. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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16 pages, 6823 KiB  
Article
Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network
by Mengtian Cui, Songlin Long, Yue Jiang and Xu Na
Entropy 2022, 24(10), 1373; https://doi.org/10.3390/e24101373 - 27 Sep 2022
Cited by 2 | Viewed by 2005
Abstract
The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a [...] Read more.
The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models. Full article
(This article belongs to the Special Issue Complexity, Entropy and the Physics of Information)
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