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Journal articles on the topic 'Co-Networks'

Author: Grafiati

Published: 25 May 2024

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1

Didegah, Fereshteh, and Mike Thelwall. "Co-saved, co-tweeted, and co-cited networks." Journal of the Association for Information Science and Technology 69, no. 8 (May 14, 2018): 959–73. http://dx.doi.org/10.1002/asi.24028.

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Díaz, David Díaz. "Amphiphilic Polymer Co-Networks." Gels 6, no. 2 (June 10, 2020): 18. http://dx.doi.org/10.3390/gels6020018.

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Amphiphilic Polymer Co-networks: Synthesis, Properties, Modelling and Applications is a new and very interesting book published by the Royal Society of Chemistry and edited by Prof. Costas S. Patrickios (University of Cyprus). Herein, a brief review of the most important features of the book and its contents is provided from a personal perspective.

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Latif, Atefeh, Alireza Hedayati, and Vahe Aghazarian. "Improving Link Prediction in Dynamic Co-authorship Social Networks." International Academic Journal of Science and Engineering 05, no. 01 (June 1, 2018): 222–40. http://dx.doi.org/10.9756/iajse/v5i1/1810020.

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Juan, David, Florencio Pazos, and Alfonso Valencia. "Co-evolution and co-adaptation in protein networks." FEBS Letters 582, no. 8 (February 20, 2008): 1225–30. http://dx.doi.org/10.1016/j.febslet.2008.02.017.

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Stoica, Adelina-Alexandra. "Homophily in co-autorship networks." International Review of Social Research 8, no. 2 (December 1, 2018): 119–28. http://dx.doi.org/10.2478/irsr-2018-0014.

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Abstract The main purpose of this paper is to measure the impact that homophily, structural characteristics of the networks, number of citations of the alters and their Hirsch score have on the number of citations of an ego. I have chosen co-authorship networks as a subject of research because they have a great influence on knowledge and on the diffusion of ideas. The studied populations are represented by full-time academics affiliated to sociology departments in Romania, Poland and Slovenia. Ego-network analysis was used as research design. The data was analyzed using linear hierarchical regression. For all three populations the average number of citations of the alter has a considerable positive impact on the number of citations of the ego. Conversely, the Hirsch score of the alter has a negative impact on the number of citations of the ego. The data analyzed in this article claims that the assumptions about the positive impact of alter citations, network size and the betweenness score on the number of the authors citations are supported empirically.

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Praczyk, Tomasz. "Cooperative co–evolutionary neural networks." Journal of Intelligent & Fuzzy Systems 30, no. 5 (April 2, 2016): 2843–58. http://dx.doi.org/10.3233/ifs-162095.

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Romero, David, Arturo Molina, and Luis M. Camarinha-Matos. "Co-innovation and collaborative networks." Production Planning & Control 22, no. 5-6 (June 16, 2011): 445–46. http://dx.doi.org/10.1080/09537287.2010.537900.

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Laird, Angela R., Simon B. Eickhoff, Claudia Rottschy, Danilo Bzdok, Kimberly L. Ray, and Peter T. Fox. "Networks of task co-activations." NeuroImage 80 (October 2013): 505–14. http://dx.doi.org/10.1016/j.neuroimage.2013.04.073.

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GOZUBUYUK, REMZI. "CO-EVOLUTION OF MULTIPLE NETWORKS." Academy of Management Proceedings 2008, no. 1 (August 2008): 1–6. http://dx.doi.org/10.5465/ambpp.2008.33631531.

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Yu, Qi, Chao Long, Yanhua Lv, Hongfang Shao, Peifeng He, and Zhiguang Duan. "Predicting Co-Author Relationship in Medical Co-Authorship Networks." PLoS ONE 9, no. 7 (July 3, 2014): e101214. http://dx.doi.org/10.1371/journal.pone.0101214.

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Eriksson, Rikard H., and Balázs Lengyel. "Co-worker Networks and Agglomeration Externalities." Economic Geography 95, no. 1 (October 9, 2018): 65–89. http://dx.doi.org/10.1080/00130095.2018.1498741.

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Larrosa, Juan M. C. "Co-authorship networks of Argentine economists." Journal of Economics, Finance and Administrative Science 24, no. 47 (April 29, 2019): 82–96. http://dx.doi.org/10.1108/jefas-06-2018-0062.

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Purpose This paper aims to provide information about the structure of collaborative work among Argentinian economics. The study provides specific applied research of social network analysis focus on this profession in this specific country. Design/methodology/approach The contribution opted for applying social network analysis tools to papers presented in a congress and published in its proceedings. The authors focus in detecting main actors, groups of co-authorship, professionals acting as bridges between groups and differences between genders. Findings The paper provides empirical insights about how co-authorship has evolved between Argentine economists. The authors find that structural properties of the network, main actors, both male and female, main universities or center that affiliates them, a gender gap that might be closing out. Research limitations/implications The paper focuses on the network for the period 1964-2014 without a more detailed dynamic. It also does not explain main topics worked by the authors. Practical implications The work provides knowledge about how groups are created in Economics in Argentina, how cooperation has evolved and what has been the role of women in this development. It also shows how different departments and entities collaborate with diverse success in the creation of new knowledge in Economics in Argentina. Originality/value The paper works with data from a source of information non-previously studied and contributes in explaining a particular type of collaborative work in a profession in Argentina.

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Su, Guifeng, Zhongyuan Ruan, Shuguang Guan, and Zonghua Liu. "Explosive synchronization on co-evolving networks." EPL (Europhysics Letters) 103, no. 4 (August 1, 2013): 48004. http://dx.doi.org/10.1209/0295-5075/103/48004.

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14

Hodgson, V., and D. McConnell. "Co-operative learning and development networks." Journal of Computer Assisted Learning 11, no. 4 (December 1995): 210–24. http://dx.doi.org/10.1111/j.1365-2729.1995.tb00137.x.

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15

Zhao, Jing, Guo-Hui Ding, Lin Tao, Hong Yu, Zhong-Hao Yu, Jian-Hua Luo, Zhi-Wei Cao, and Yi-Xue Li. "Modular co-evolution of metabolic networks." BMC Bioinformatics 8, no. 1 (2007): 311. http://dx.doi.org/10.1186/1471-2105-8-311.

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16

Lacam, Jean Sébastien, and David Salvetat. "The complexity of co-opetitive networks." Business Process Management Journal 23, no. 1 (February 3, 2017): 176–95. http://dx.doi.org/10.1108/bpmj-12-2015-0187.

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Purpose Many firms engage in co-opetitive projects during which they have simultaneously competitive and collaborative relationships with many rivals in a complex network. A co-opetitive network offers them access to a large volume of resources and knowledge, for example, to support new markets and/or territories. So, does the network grow with the scope of the co-opetition project? The paper aims to discuss this issue. Design/methodology/approach An empirical study of 106 French boating intermediate-sized enterprises (ETIs) and small and medium enterprises provides a descriptive and explanatory analysis of co-opetitive networks. Findings The results support this definition of a complex co-opetitive network only when the objectives of a firm are part of the geographical expansion of its activities. In contrast, these relations remain simple (dyadic) when a firm favours a strategy of diversifying its activities while maintaining its unique local geographical market. Research limitations/implications First, the work is based on a quantitative methodology, so is static. It would be interesting to analyze the process of the building of co-opetitive relationships and opportunism between rival firms, for example, through a qualitative study. Second, this work focusses on boating companies in France. It may be appropriate to consider the sanctions placed on the opportunism of foreign firms in co-opetition. Third, future work could increase understanding, not only of the nature of reprisals inflicted on individualistic co-opetitors, but also on the structure, objectives and results of these reprisals. Originality/value The study deepens our knowledge of the definition, composition and determinants of co-opetitive networks.

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17

Lee, Chang-Yong. "Characteristics of microRNA co-target networks." Physica A: Statistical Mechanics and its Applications 390, no. 14 (July 2011): 2728–37. http://dx.doi.org/10.1016/j.physa.2011.03.031.

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18

Jaakkola, Elina, and Taru Hakanen. "Value co-creation in solution networks." Industrial Marketing Management 42, no. 1 (January 2013): 47–58. http://dx.doi.org/10.1016/j.indmarman.2012.11.005.

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19

Brandsen, Taco, and Eelco van Hout. "Co-management in public service networks." Public Management Review 8, no. 4 (December 2006): 537–49. http://dx.doi.org/10.1080/14719030601022908.

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20

Rangwala, Huzefa, and Salman Jamali. "Co-Participation Networks Using Comment Information." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (May 16, 2010): 315–18. http://dx.doi.org/10.1609/icwsm.v4i1.14035.

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Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. We use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We also compare network properties of our co-participation network to a previously defined reply-answer network on news forums.

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21

Brodersen, Søsser, and Signe Pedersen. "Navigating co-design of actor-networks." International Journal of Healthcare Technology and Management 20, no. 2 (2023): 115–25. http://dx.doi.org/10.1504/ijhtm.2023.10056910.

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Pedersen, Signe, and Søsser Brodersen. "Navigating co-design of actor-networks." International Journal of Healthcare Technology and Management 20, no. 2 (2023): 115–25. http://dx.doi.org/10.1504/ijhtm.2023.131516.

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23

Holguín, Sandie, and Jennifer J. Davis. "Economic Autonomy, Networks, and Co-optation." Journal of Women's History 35, no. 2 (June 2023): 5–10. http://dx.doi.org/10.1353/jowh.2023.a899536.

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24

Hasin, Hajar Ali, and Diman Hassan. "Link Prediction in Co-authorship Networks." Science Journal of University of Zakho 10, no. 4 (November 7, 2022): 235–57. http://dx.doi.org/10.25271/sjuoz.2022.10.4.1040.

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Besides social network analysis, the Link-Prediction (LP) problem has useful applications in information retrieval, bioinformatics, telecommunications, microbiology, and e-commerce as a forecast of future links in a given context to find what possible connections are based on a local and global statistical analysis of the given graph data. However, in Academic Social Networks (ASNs), the LP issue has recently attracted a lot of attention in academia and called for a variety of link prediction techniques to predict co-authorship among researchers and to examine the rich structural and associated data. As a result, this study investigates the problem of LP in ASNs to forecast the upcoming co-authorships among researchers. In a systematic approach, this review presents, analyses, and compares the primary taxonomies of topological-based, content-based, and hybrid-based approaches, which are used for computing similar scores for each pair of unconnected nodes. Then, this study ends with findings on challenges and open problems for the community to work on for further development of the LP problem of scholarly social networks.

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25

Ng, Desmond, Steven Sonka, and Randall Westgren. "Co-evolutionary Processes in Supply Chain Networks." Journal on Chain and Network Science 3, no. 1 (June 1, 2003): 45–58. http://dx.doi.org/10.3920/jcns2003.x029.

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This paper presents an innovative conceptual and analytical framework to enhance understanding of the dynamic processes of food supply chains. The framework employs a co-evolutionary perspective to exploit complex and dynamic relationships spanning different levels of aggregation. To make this co-evolutionary perspective operational, the paper's conceptual approach draws from complexity theory, Austrian economics, and social networks. This approach not only improves our understanding of the conflicting dynamic influences that impact food market systems but also presents an alternative viewpoint of organizational interrelationships. In addition, this framework provides analytical advances through the use of agent-based modelling in an agricultural supply chain context. Simulation results are presented to support the arguments of this nested co-evolutionary framework.

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26

Mahmood, Basim, Nagham A. Sultan, Karam H. Thanoon, and Dheyaa S. Kadhim. "Measuring scientific collaboration in co-authorship networks." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 1103. http://dx.doi.org/10.11591/ijai.v10.i4.pp1103-1114.

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<span lang="EN-US">Scientific research is currently considered one of the key factors in the development of our life. It plays a significant role in managing our business, study, and work more conveniently. One of the important aspects when it comes to scientific research is the level of collaboration among researchers/disciplines. The collaboration between two different disciplines contributes to obtaining more reliable solutions for our everyday issues. Therefore, it is needed to understand the collaboration patterns among researchers and come up with convenient strategies for strengthening this kind of collaboration. In this work, we aim at investigating the patterns of scientific collaboration among researchers across disciplines. To this end, we generate a co-authorship network for several disciplines. The generated network reveals many interesting facts regarding the collaboration patterns among researchers who work in the same/different disciplines. We involve several measurements in this study that evaluate different aspects, which is of interest to the research communities since most of the studies in the literature measure specific aspects. Moreover, we propose a novel metric for measuring scientific collaboration in a research community and use it to benchmark the collaboration among disciplines. Finally, we use the obtained results/facts in providing recommendations for scientific communities.</span>

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27

Fang, Jinyuan, Shangsong Liang, Zaiqiao Meng, and Maarten De Rijke. "Hyperspherical Variational Co-embedding for Attributed Networks." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–36. http://dx.doi.org/10.1145/3478284.

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Network-based information has been widely explored and exploited in the information retrieval literature. Attributed networks, consisting of nodes, edges as well as attributes describing properties of nodes, are a basic type of network-based data, and are especially useful for many applications. Examples include user profiling in social networks and item recommendation in user-item purchase networks. Learning useful and expressive representations of entities in attributed networks can provide more effective building blocks to down-stream network-based tasks such as link prediction and attribute inference. Practically, input features of attributed networks are normalized as unit directional vectors. However, most network embedding techniques ignore the spherical nature of inputs and focus on learning representations in a Gaussian or Euclidean space, which, we hypothesize, might lead to less effective representations. To obtain more effective representations of attributed networks, we investigate the problem of mapping an attributed network with unit normalized directional features into a non-Gaussian and non-Euclidean space. Specifically, we propose a hyperspherical variational co-embedding for attributed networks (HCAN), which is based on generalized variational auto-encoders for heterogeneous data with multiple types of entities. HCAN jointly learns latent embeddings for both nodes and attributes in a unified hyperspherical space such that the affinities between nodes and attributes can be captured effectively. We argue that this is a crucial feature in many real-world applications of attributed networks. Previous Gaussian network embedding algorithms break the assumption of uninformative prior, which leads to unstable results and poor performance. In contrast, HCAN embeds nodes and attributes as von Mises-Fisher distributions, and allows one to capture the uncertainty of the inferred representations. Experimental results on eight datasets show that HCAN yields better performance in a number of applications compared with nine state-of-the-art baselines.

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Chen, Ming, Yunhao Li, and Xiuze Zhou. "CoNet: Co-occurrence neural networks for recommendation." Future Generation Computer Systems 124 (November 2021): 308–14. http://dx.doi.org/10.1016/j.future.2021.06.008.

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Ioannidis, Evangelos, Nikos Varsakelis, and Ioannis Antoniou. "Intelligent Agents in Co-Evolving Knowledge Networks." Mathematics 9, no. 1 (January 5, 2021): 103. http://dx.doi.org/10.3390/math9010103.

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We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.

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Zhang, Jiekuan. "Tourism Research Co-authorship Networks in China." Journal of China Tourism Research 11, no. 4 (October 2, 2015): 424–39. http://dx.doi.org/10.1080/19388160.2015.1108889.

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31

Rivkin, Steven R. "Co-Evolution of Electric & Telecommunications Networks." Electricity Journal 11, no. 4 (May 1998): 71–76. http://dx.doi.org/10.1016/s1040-6190(98)00036-0.

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32

Frank, Ove. "Statistical estimation of co-offending youth networks." Social Networks 23, no. 3 (July 2001): 203–14. http://dx.doi.org/10.1016/s0378-8733(01)00040-5.

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33

Lacoste, Sylvie. "Sustainable value co-creation in business networks." Industrial Marketing Management 52 (January 2016): 151–62. http://dx.doi.org/10.1016/j.indmarman.2015.05.018.

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34

Serna, L., C. Merlo, M. Zolghadri, and S. Minel. "Actors’ networks management for design co-ordination." International Journal on Interactive Design and Manufacturing (IJIDeM) 5, no. 1 (January 13, 2011): 67–71. http://dx.doi.org/10.1007/s12008-010-0115-3.

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35

Carson, Matthew B., Nitin Bhardwaj, and Hui Lu. "Investigating Co-Regulation Networks using Generative Models." Biophysical Journal 98, no. 3 (January 2010): 740a. http://dx.doi.org/10.1016/j.bpj.2009.12.4060.

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Grunow, M., H. O. Günther, and G. Yang. "Plant co-ordination in pharmaceutics supply networks." OR Spectrum 25, no. 1 (February 1, 2003): 109–41. http://dx.doi.org/10.1007/s00291-002-0117-z.

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37

Fischbach, Kai, Johannes Putzke, and Detlef Schoder. "Co-authorship networks in electronic markets research." Electronic Markets 21, no. 1 (February 2011): 19–40. http://dx.doi.org/10.1007/s12525-011-0051-5.

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38

Constantin, Adrian, Arne Ellerbrock, Flavio Fernandez, and Johannes Ruess. "Co-Simulation of Power Electronic Dominated Networks." IEEE Power and Energy Magazine 18, no. 2 (March 2020): 84–89. http://dx.doi.org/10.1109/mpe.2020.2974601.

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Ahmed, Hasin Afzal, Priyakshi Mahanta, Dhruba Kr Bhattacharyya, and Jugal Kr Kalita. "Module extraction from subspace co-expression networks." Network Modeling Analysis in Health Informatics and Bioinformatics 1, no. 4 (September 29, 2012): 183–95. http://dx.doi.org/10.1007/s13721-012-0018-2.

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Kalluru, Vikram, Raghu Machiraju, and Kun Huang. "Identify condition-specific gene co-expression networks." International Journal of Computational Biology and Drug Design 6, no. 1/2 (2013): 50. http://dx.doi.org/10.1504/ijcbdd.2013.052201.

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Poocharoen, Ora-orn, and Bernard Ting. "Collaboration, Co-Production, Networks: Convergence of theories." Public Management Review 17, no. 4 (December 18, 2013): 587–614. http://dx.doi.org/10.1080/14719037.2013.866479.

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Leifeld, Philip, and Karin Ingold. "Co-authorship Networks in Swiss Political Research." Swiss Political Science Review 22, no. 2 (December 28, 2015): 264–87. http://dx.doi.org/10.1111/spsr.12193.

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Goh, Woon Peng, Kang-Kwong Luke, and Siew Ann Cheong. "Functional shortcuts in language co-occurrence networks." PLOS ONE 13, no. 9 (September 11, 2018): e0203025. http://dx.doi.org/10.1371/journal.pone.0203025.

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García-Santiago, Lola, and Felix De Moya-Anegón. "Using co-outlinks to mine heterogeneous networks." Scientometrics 79, no. 3 (January 31, 2009): 681–702. http://dx.doi.org/10.1007/s11192-007-1855-0.

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Aleroud, Ahmed, and Aryya Gangopadhyay. "Multimode co-clustering for analyzing terrorist networks." Information Systems Frontiers 20, no. 5 (October 7, 2016): 1053–74. http://dx.doi.org/10.1007/s10796-016-9712-4.

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Devogelaer, Jan‐Joris, Hugo Meekes, Paul Tinnemans, Elias Vlieg, and René Gelder. "Co‐crystal Prediction by Artificial Neural Networks**." Angewandte Chemie 132, no. 48 (September 18, 2020): 21895–902. http://dx.doi.org/10.1002/ange.202009467.

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Devogelaer, Jan‐Joris, Hugo Meekes, Paul Tinnemans, Elias Vlieg, and René Gelder. "Co‐crystal Prediction by Artificial Neural Networks**." Angewandte Chemie International Edition 59, no. 48 (September 18, 2020): 21711–18. http://dx.doi.org/10.1002/anie.202009467.

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48

Kumar, Sameer. "Co-authorship networks: a review of the literature." Aslib Journal of Information Management 67, no. 1 (January 19, 2015): 55–73. http://dx.doi.org/10.1108/ajim-09-2014-0116.

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Purpose – The purpose of this paper is to attempt to provide a review of the growing literature on co-authorship networks and the research gaps that may be investigated for future studies in this field. Design/methodology/approach – The existing literature on co-authorship networks was identified, evaluated and interpreted. Narrative review style was followed. Findings – Co-authorship, a proxy of research collaboration, is a key mechanism that links different sets of talent to produce a research output. Co-authorship could also be seen from the perspective of social networks. An in-depth analysis of such knowledge networks provides an opportunity to investigate its structure. Patterns of these relationships could reveal, for example, the mechanism that shapes our scientific community. The study provides a review of the expanding literature on co-authorship networks. Originality/value – This is one of the first comprehensive reviews of network-based studies on co-authorship. The field is fast evolving, opening new gaps for potential research. The study identifies some of these gaps.

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49

Basit, Abdul, Siti Munfarida, Rully Rully, and Mateo Jose A. Vidal. "Co-branding of compass shoes on social networks." Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies) 5, no. 2 (June 30, 2021): 302–18. http://dx.doi.org/10.25139/jsk.v5i2.3759.

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Today's competition requires each product to develop a co-branding strategy in reaching the market, especially in the digital era where social networking is a space to build brand identities. So this research was conducted to find out how to co-brand Compass Shoes on social networks. To find out how much influence the co-branding variable has on social networks, seen from the dimensions of social networks by measuring social networks—using a quantitative-explanatory methodology, a data collection using a random questionnaire with a total of 398 respondents who meet the characteristics of the sample. The results of this study have a hypothesis where the exogenous variable, namely co-branding, has a significant effect on the endogenous variable, namely social networking. Creating co-branding, of course, must have a strong strategy in innovation and be able to understand consumer desires by providing collaboration space to build a shared identity.

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50

Čaić, Martina, Gaby Odekerken-Schröder, and Dominik Mahr. "Service robots: value co-creation and co-destruction in elderly care networks." Journal of Service Management 29, no. 2 (March 12, 2018): 178–205. http://dx.doi.org/10.1108/josm-07-2017-0179.

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Purpose The purpose of this paper is to investigate the potential roles for service robots (i.e. socially assistive robots) in value networks of elderly care. Taking an elderly person’s perspective, it defines robot roles according to their value co-creating/destroying potential for the elderly user (i.e. focal actor), while acknowledging consequences for a network of users around the elderly (i.e. network actors). Design/methodology/approach This qualitative, interpretative study employs in-depth phenomenographic interviews, supported by generative cards activities (i.e. Contextual Value Network Mapping), to elicit an elderly person’s tacit knowledge and anticipate the effects of introducing an automated actor on institutionalized value co-creation practices. Findings The proposed typology identifies six roles of socially assistive robots in an elderly person’s value network (enabler, intruder, ally, replacement, extended self, and deactivator) and links them to three health-supporting functions by robots: safeguarding, social contact, and cognitive support. Research limitations/implications Elderly people have notable expectations about the inclusion of a socially assistive robot as a new actor in their value networks. The identified robot roles inform service scholars and managers about both the value co-destruction potential that needs to be avoided through careful designs and the value co-creation potential that should be leveraged. Originality/value Using network-conscious phenomenographic interviews before the introduction of a novel value proposition sheds new light on the shifting value co-creation interplay among value network actors (i.e. elderly people, formal and informal caregivers). The value co-creation/destruction potential of socially assistive robots and their corresponding roles in care-based value networks offer insights for the design of meaningful robotic technology and its introduction into the existing service networks.

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