Peng Mei¹, Gangyi Ding¹, Qiankun Jin¹, Fuquan Zhang^2,*, Yeh-Cheng Chen³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 159-171, 2021, DOI:10.32604/iasc.2021.012813

Abstract Community structure is a key component in complex network systems. This paper aims to improve the effectiveness of community detection and community discovery in complex network systems by providing directions for the reconstruction and optimization of community structures to expand the application of intelligent optimization algorithms in community structures. First, deep learning algorithms and ant colony algorithms are used to elaborate the community detection and community discovery in complex networks. Next, we introduce the technology of transfer learning and propose an algorithm of deep self-encoder modeling based on transfer learning (DSEM-TL). The DSEM-TL algorithm’s indicators include normalized mutual information and… More >

SHYAM BADU¹, SANJAY PRABHAKAR^1,2, RODERICK MELNIK^1,3,*

BIOCELL, Vol.44, No.3, pp. 279-291, 2020, DOI:10.32604/biocell.2020.010916

Abstract Photosynthesis is a fundamental process in biosciences and biotechnology that influences profoundly the research in other disciplines. In this paper, we focus on the characterization of fundamental components, present in pigment-protein complexes, in terms of their spectroscopic properties such as infrared spectra, nuclear magnetic resonance, as well as nuclear quadrupole resonance, which are of critical importance for many applications. Such components include chlorophylls and bacteriochlorophylls. Based on the density functional theory method, we calculate the main spectroscopic characteristics of these components for the Fenna-Matthews-Olson light-harvesting complex, analyze them and compare them with available experimental results. Future outlook is discussed in… More >

Hui Wang, Liang Li, Long-yun Gao, Wu Chen

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 35-40, 2018, DOI:10.1080/10798587.2016.1267238

Abstract With the development of big data science, handling intensive knowledge in the complex network becomes more and more important. Knowledge representation of multi-agent negotiation in the complex network plays an important role in big data science. As a modern approach to declarative programming, answer set programming is widely applied in representing the multi-agent negotiation knowledge in recent years. But almost all the relevant negotiation models are based on complete rational agents, which make the negotiation process complex and low efficient. Sorting negotiation demands is the most key step in creating an efficient negotiation model to improve the negotiation ability of… More >

Tong Zhao, Bangwen Peng

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 725-733, 2019, DOI:10.31209/2019.100000076

Abstract In this article, the industry complex network of China’s manufacturing is built based on the central input flow matrix data of 2012 Input-Output Tables of China through industry network modeling. This article analyses the complex nature of China’s manufacturing network in three aspects, which are feature of industry network in general, community structure and industry nodes, using a series of statistics measuring complex network. More >

Qiang Wei^{1, 2, *}, Guangmin Hu¹, Chao Shen³, Yunfei Yin^{4, 5}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 705-724, 2020, DOI:10.32604/cmc.2020.07467

Abstract Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring. However, the existing methods are time-consuming for even moderate-scale networks. In this paper, we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries. The effectiveness of the proposed method is validated through synthetic and real-world networks. The experimental results show that our method is 10⁵ times faster than the existing methods and can solve the shortest-path… More >

Ning Cao^1,2, Shengfang Li¹, Keyong Shen¹, Sheng Bin³, Gengxin Sun^3,*, Dongjie Zhu⁴, Xiuli Han⁵, Guangsheng Cao⁵, Abraham Campbell⁶

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 227-241, 2019, DOI:10.32604/cmc.2019.06125

Abstract Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics mining method of OD flows… More >

Lingling Xia¹, Bo Song^2,3, Zhengjun Jing⁴, Yurong Song^5,*, Liang Zhang¹

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 123-144, 2018, DOI:10.32604/cmc.2018.03738

Abstract Considering dynamical disease spreading network consisting of moving individuals, a new double-layer network is constructed, one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves. On the basis of Markov chains theory, a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment. Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence… More >