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Journal articles on the topic 'Networx'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Fortney, Kristen, Wing Xie, Max Kotlyar, Joshua Griesman, Yulia Kotseruba, and Igor Jurisica. "NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae." Nucleic Acids Research 41, no. D1 (2012): D720—D727. http://dx.doi.org/10.1093/nar/gks1106.

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2

Mufadhol, Mufadhol, Guruh Aryotejo, and Agus Wibowo. "Netscan and Networx for Management Bandwidth and Traffic with Simple Routing." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 1 (2017): 464. http://dx.doi.org/10.12928/telkomnika.v15i1.4183.

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3

Mufadhol, Mufadhol, Aryotejo Guruh, and Wibowo Agus. "Netscan and Networx for Management Bandwidth and Traffic with Simple Routing." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 464–70. https://doi.org/10.12928/TELKOMNIKA.v15i1.4183.

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Currently, approximately 3.4 billion people are accessing the internet around the world and that number is still growing. This phenomenon creates the Internet culture that has a great influence on science, technology and even the world economy. Internet bandwidth is very expensive, which makes an information technology based company have to map the internet accurately daily, monthly or yearly. That data will be taken into consideration by the company to decide how to provide services that make an internet surfing is a pleasant experience. If not regulated, most likely the traffic and bandwidth

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4

Burhanuddin, Burhanuddin. "ANALISIS KINERJA JARINGAN WIRELESS LAN DENGAN MENGGUNAKAN METODE QUALITY OF SERVICE (QOS)." Jurnal Teknologi Terapan and Sains 4.0 1, no. 2 (2020): 241. http://dx.doi.org/10.29103/tts.v1i2.3264.

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Perkembangan teknologi saat ini sudah demikian maju, terutama dalam bidang telekomunikasi. Teknologi wireless LAN merupakan salah satu terobosan terbaru yang memberikan kemudahan dalam pertukaran data antara pengguna dalam satu jaringan yang memungkinkan pengguna dapat saling berkomunikasi. Pentingnya standar kualitas pelayanan kinerja jaringan untuk memberikan layanan yang baik sesuai dengan standar parameter Quality of Service (QoS). Standar pelayanan kualitas kinerja jaringan wireless yang memuaskan bagi pengguna. Dengan menerapkannya standar Quality of Service (QoS), maka dapat diketahui k

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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Shydlovskyi, P., and Ya Morozova. "FOREWORD: Network Approach for Studying the Prehistoric Networks." Vita Antiqua 1, no. 10 (2018): 6–12. http://dx.doi.org/10.37098/2519-4542-2018-1-10-6-12.

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8

THATIPAMULA RAJU, THATIPAMULA RAJU, and D. DEEPIKA RANI D. DEEPIKA RANI. "Achieving Network Level Privacy in Wireless Sensor Networks." International Journal of Scientific Research 2, no. 8 (2012): 183–87. http://dx.doi.org/10.15373/22778179/aug2013/61.

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9

Yi-Wei Ma, Yi-Wei Ma, Jiann-Liang Chen Yi-Wei Ma, Yu-Liang Tang Jiann-Liang Chen, and Kuan-Hung Lai Yu-Liang Tang. "Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks." 網際網路技術學刊 23, no. 5 (2022): 1087–97. http://dx.doi.org/10.53106/160792642022092305017.

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<p>This work proposes an adaptive resource orchestration system for a Wireless Local Area Network (WLAN) that is based on the operating principle of Cognitive Radio (CR) technology. By collecting environmental parameters, including the retransmission rate and the channel occupancy rate, the proposed system has &ldquo;knowledge&rdquo; of overall transmission behavior and can regulate transmission resources. An Adaptive Connection Assignment (ACA) mechanism is proposed for end devices; it find out target end devices with poor transmission performance, analyzes their alternative Acc

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10

HRABCAK, David, and Lubomir DOBOS. "THE CONCEPT OF MULTILAYERED NETWORK MODEL FOR 5G NETWORKS." Acta Electrotechnica et Informatica 19, no. 3 (2019): 39–43. http://dx.doi.org/10.15546/aeei-2019-0022.

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11

Yiyuan Xie, Yiyuan Xie, and Zhu Yang Zhu Yang. "All-optical network interface from backbone networks to local area networks based on semiconductor optical amplifiers." Chinese Optics Letters 11, no. 11 (2013): 110605–8. http://dx.doi.org/10.3788/col201311.110605.

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12

Hari, Parli Baijnath, and Shailendra Narayan Singh. "Security Attacks at MAC and Network Layer in Wireless Sensor Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (2019): 82–89. http://dx.doi.org/10.5373/jardcs/v11i12/20193215.

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13

Liu, Wen Wei, and Dan Wang. "On Link Density and Network Synchronization in Scale-Free Network." Applied Mechanics and Materials 380-384 (August 2013): 2276–79. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2276.

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The relations between link density and network synchronizability based on scale-free weighted networks is investigated. In this work, it shows that synchronizability of networks Type I decrease along with the increases of link density, when the netwrok size is fixed. While the synchronizability of networks Type II is remarkable decreased by enhancing the link density with different network size.

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14

Koam, Ali N. A., Ali Ahmad, and Yasir Ahmad. "Computation of reverse degree-based topological indices of hex-derived networks." AIMS Mathematics 6, no. 10 (2021): 11330–45. http://dx.doi.org/10.3934/math.2021658.

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<abstract><p>Network theory gives an approach to show huge and complex frameworks through a complete arrangement of logical devices. A network is made is made of vertices and edges, where the degree of a vertex refers to the number of joined edges. The degree appropriation of a network represents the likelihood of every vertex having a particular degree and shows significant worldwide network properties. Network theory has applications in many disciplines like basic sciences, computer science, engineering, medical, business, public health and sociology. There are some important net

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15

Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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16

Fu Jie Tey, Fu Jie Tey, Tin-Yu Wu Fu Jie Tey, Yueh Wu Tin-Yu Wu, and Jiann-Liang Chen Yueh Wu. "Generative Adversarial Network for Simulation of Load Balancing Optimization in Mobile Networks." 網際網路技術學刊 23, no. 2 (2022): 297–304. http://dx.doi.org/10.53106/160792642022032302010.

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<p>The commercial operation of 5G networks is almost ready to be launched, but problems related to wireless environment, load balancing for example, remain. Many load balancing methods have been proposed, but they were implemented in simulation environments that greatly differ from 5G networks. Current load balancing algorithms, on the other hand, focus on the selection of appropriate Wi-Fi or macro & small cells for Device to Device (D2D) communications, but Wi-Fi facilities and small cells are not available all the time. For this reason, we propose to use the macro cells that provi

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17

Navghare, Tukaram, Aniket Muley, and Vinayak Jadhav. "Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach." Indian Journal Of Science And Technology 17, no. 4 (2024): 352–58. http://dx.doi.org/10.17485/ijst/v17i4.3018.

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18

Boroojeni, Asma Azizi, Jeremy Dewar, Tong Wu, and James M. Hyman. "Generating bipartite networks with a prescribed joint degree distribution." Journal of Complex Networks 5, no. 6 (2017): 839–57. http://dx.doi.org/10.1093/comnet/cnx014.

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Abstract We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree–degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor–movie networks, author–article networks, co-occurrence networks and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbours of that node that must be preserved when generating a network that reflects the structure of the underling sys

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19

Kim, Junki. "Networks, Network Governance, and Networked Networks." International Review of Public Administration 11, no. 1 (2006): 19–34. http://dx.doi.org/10.1080/12294659.2006.10805075.

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20

Ingole, Piyush K., Apeksha V. Sakhare, and Samir N. Ajani. "Modality of Multi-Attribute Decision Making for Network Selection in Heterogeneous Wireless Networks." AMBIENT SCIENCE 9, no. 01 (2022): 26–31. http://dx.doi.org/10.21276/ambi.2022.09.2.ta02.

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21

Ren Danping, 任丹萍, 张黎 Zhang Li та 胡劲华 Hu Jinhua. "空分复用弹性光网络中串扰感知的虚拟网络映射算法". Acta Optica Sinica 43, № 5 (2023): 0506003. http://dx.doi.org/10.3788/aos221401.

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22

Naranjo Orovio, Consuelo. "A network of networks." HAU: Journal of Ethnographic Theory 11, no. 1 (2021): 52–66. http://dx.doi.org/10.1086/713372.

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23

Iedema, Rick, Raj Verma, Sonia Wutzke, Nigel Lyons, and Brian McCaughan. "A network of networks." Journal of Health Organization and Management 31, no. 2 (2017): 223–36. http://dx.doi.org/10.1108/jhom-07-2016-0146.

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Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author’s participation in network activities and discussions with the agency’s staff about their main objectives, challenges and achievemen

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24

Hepworth, H. Philip. "A network of networks." International Social Work 30, no. 1 (1987): 5–9. http://dx.doi.org/10.1177/002087288703000102.

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25

Fuengfusin, Ninnart, and Hakaru Tamukoh. "Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 191–94. http://dx.doi.org/10.5954/icarob.2020.os20-2.

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26

O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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27

Murugan, S., and Dr M. Jeyakarthic. "Optimal Deep Neural Network based Classification Model for Intrusion Detection in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1374–87. http://dx.doi.org/10.5373/jardcs/v11sp10/20192983.

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28

Walker, David M., Antoinette Tordesillas, Amy L. Rechenmacher, and Michael Small. "Multiscale resolution of networks of granular media network evolution—a network of networks." IEICE Proceeding Series 2 (March 17, 2014): 294–97. http://dx.doi.org/10.15248/proc.2.294.

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29

Smita, Sandeep Muley. "A Review on Graph Theory and Its Implementation Using Python Programming." International Journal of Advance and Applied Research S6, no. 22 (2025): 1124–26. https://doi.org/10.5281/zenodo.15542617.

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<em>Graph theory is a foundation of modern computer science and mathematics, with applications spanning from network analysis to routing algorithms and social network analysis. It is a mathematical framework used to study relationships between objects represented as nodes connected by edges. It has applications in numerous domains, such as network analysis, social networks, biology, and more. This paper provides a review of the core concepts and algorithms in graph theory, with a focus on Python libraries that are widely used for graph manipulation, including NetworkX. The paper aims to offer

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30

Ali Alshami, Yousef, and Shiv Kumar. "Prevent the Eavesdropping in D2D Networks by Using Network Coding and Signal-To-Noise (SNR)." International Journal of Science and Research (IJSR) 10, no. 1 (2021): 1564–72. https://doi.org/10.21275/sr21112225156.

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31

Gorbachev, Alexander. "МАСКИРОВАНИЕ ТОПОЛОГИЧЕСКИХ СВОЙСТВ ВЫЧИСЛИТЕЛЬНЫХ СЕТЕЙ В УСЛОВИЯХ СЕТЕВОЙ РАЗВЕДКИ. Часть 2". Voprosy kiberbezopasnosti 1, № 65 (2025): 63–72. https://doi.org/10.21681/2311-3456-2025-1-63-72.

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Цель исследования: разработка системы моделей, включающей классические модели случайных графов и генеративные модели искусственного интеллекта и предназначенной для решения задачи маскирования топологических свойств вычислительных сетей при генерации ложного сетевого трафика и применении ложных сетевых информационных объектов, позволяющих, с одной стороны, обеспечить заданную степень сходства топологических свойств реальных вычислительных сетей с ложными, а с другой стороны, максимизировать показатель защищенности критических узлов реальных вычислительных сетей. Используемые методы: взвешенная

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32

TORAN, CHAITANYA. "AI-Powered Route Selection in Ad Hoc Wireless Networks." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44739.

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ABSTRACT - Ad hoc wireless networks (AHWNs) are decentralized and change without warning; therefore, selecting a good route presents a key problem. Established routing protocols battle to change to many network topology changes. They also have a hard time with diverse link conditions. In this study, we propose an AI-supported method for picking routes that uses a Random Forest Classifier (RFC) to make the best choice of paths in AHWNs. We use network graph modeling, feature extraction, and machine learning to forecast the optimal routes according to node attributes. We use NetworkX to create a

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33

Han Zengfu, Kong Jiankun, Wang Zhiguo, et al. "AI-based network topology optimization system." ITU Journal on Future and Evolving Technologies 2, no. 4 (2021): 81–90. http://dx.doi.org/10.52953/yxtb5085.

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Existing network topology planning does not fully consider the increasing network traffic and problem of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments in network construction. The AI-based network topology optimization system introduced in this paper builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge deletion or addition based on traffic over nodes, and ensures network load balancing when node traffi

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34

Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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35

Fuengfusin, Ninnart, and Hakaru Tamukoh. "Convolutional Network with Sub-Networks." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 6–9. http://dx.doi.org/10.5954/icarob.2021.os19-1.

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36

Hampson, J. "Urban network development [distribution networks]." Power Engineering Journal 15, no. 5 (2001): 224–32. http://dx.doi.org/10.1049/pe:20010504.

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37

Hirai, Shigeoki. "Multimedia Networks. Network and Telerobotics." Journal of the Robotics Society of Japan 15, no. 4 (1997): 516–19. http://dx.doi.org/10.7210/jrsj.15.516.

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38

Tizghadam, Ali, Weiwei Li, and Alberto Leon-Garcia. "Network criticality in vehicular networks." ACM SIGMETRICS Performance Evaluation Review 40, no. 3 (2012): 107–9. http://dx.doi.org/10.1145/2425248.2425278.

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39

Jiang, Chunxiao, Yan Chen, K. J. Ray Liu, and Yong Ren. "Network economics in cognitive networks." IEEE Communications Magazine 53, no. 5 (2015): 75–81. http://dx.doi.org/10.1109/mcom.2015.7105644.

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40

Kilkenny, Maureen, and Nerys Fuller Love. "Network analysis and business networks." International Journal of Entrepreneurship and Small Business 21, no. 3 (2014): 303. http://dx.doi.org/10.1504/ijesb.2014.060894.

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41

Havlin, S., D. Y. Kenett, A. Bashan, J. Gao, and H. E. Stanley. "Vulnerability of network of networks." European Physical Journal Special Topics 223, no. 11 (2014): 2087–106. http://dx.doi.org/10.1140/epjst/e2014-02251-6.

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42

Rutherford, Ian. "Network Theory and Theoric Networks." Mediterranean Historical Review 22, no. 1 (2007): 23–37. http://dx.doi.org/10.1080/09518960701538523.

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43

Wang, Shijun, Zhongbao Kou, and Changshui Zhang. "Network boosting on different networks." Physica A: Statistical Mechanics and its Applications 366 (July 2006): 561–70. http://dx.doi.org/10.1016/j.physa.2005.10.023.

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44

Huang, Gang, and Mark Tausig. "Network range in personal networks." Social Networks 12, no. 3 (1990): 261–68. http://dx.doi.org/10.1016/0378-8733(90)90009-x.

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45

Stalker, James, John Lasley, George Frederick, et al. "A Nationwide Network of Networks." Bulletin of the American Meteorological Society 94, no. 10 (2013): 1602–6. http://dx.doi.org/10.1175/1520-0477-94.10.1602.

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46

Hobbs, Linn W. "Network topology in aperiodic networks." Journal of Non-Crystalline Solids 192-193 (December 1995): 79–91. http://dx.doi.org/10.1016/0022-3093(95)00431-9.

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47

Hu, Ziping, Krishnaiyan Thulasiraman, and Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure." American Journal of Operations Research 03, no. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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48

HAN, Shuangshuang, Feiyue WANG, Xiang CHENG, Xiaoya HU, Jian YANG, and Liuqing YANG. "Parallel networks and network softwarization: a novel network architecture." SCIENTIA SINICA Informationis 47, no. 7 (2017): 811. http://dx.doi.org/10.1360/n112016-00047.

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49

Gao, Jianxi, Daqing Li, and Shlomo Havlin. "From a single network to a network of networks." National Science Review 1, no. 3 (2014): 346–56. http://dx.doi.org/10.1093/nsr/nwu020.

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Abstract Network science has attracted much attention in recent years due to its interdisciplinary applications. We witnessed the revolution of network science in 1998 and 1999 started with small-world and scale-free networks having now thousands of high-profile publications, and it seems that since 2010 studies of ‘network of networks’ (NON), sometimes called multilayer networks or multiplex, have attracted more and more attention. The analytic framework for NON yields a novel percolation law for n interdependent networks that shows that percolation theory of single networks studied extensive

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50

Lundberg, Heléne, and Christina Öberg. "A network of networks: knowledge development across network boundaries." Journal of Business & Industrial Marketing 40, no. 13 (2025): 77–86. https://doi.org/10.1108/jbim-07-2024-0502.

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Purpose The purpose of this paper is to examine knowledge development across network boundaries. Design/methodology/approach The empirical part of this paper is based on a longitudinal case study of a Swedish regional strategic network (RSN) in a sparely populated area. Data was collected through interviews, websites and social media. The case focuses on the creation of sub-networks designed to improve collaboration among different experts. The paper analyses how knowledge evolved across these sub-networks, the RSN and social and business networks. Findings The case study demonstrates how RSN

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