Relevant bibliographies by topics / Co-Networks

Academic literature on the topic 'Co-Networks'

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Published: 25 May 2024

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Journal articles on the topic "Co-Networks"

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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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Dissertations / Theses on the topic "Co-Networks"

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Morel, Victor. "Generating co-evolutionary polarized opinion networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-307147.

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In this thesis we present a co-evolutionary opinion network model, which aim to efficiently represent an online social network, with each person represented as a node attributed with an opinion, and the relations between people by edges. For this, we needed to classify the existing models through the prism of co-evolution, i.e. how the topology and the state of the network interact between each other, so one can find a model of classification for network models. This model will be compared under certain aspects to empirical data, as well as previous works. We show the emergence of a polarization of the opinions in the network, which appears only under certain conditions : a strong homophily between nodes, as well as a co-evolutionary behavior, i.e. a strong interaction between the topology and the state, of the model.
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Pires, David Laranjo. "Authorship attribution using co-occurrence networks." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/30831.

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Atribuição de Autoria utlizando Redes de Co-Ocorrencia Nesta tese é abordada a tarefa de Atribuição de Autoria como uma tarefa de classificação. As metodologias utilizadas representam textos em grafos. Destes, várias medidas são extraídas, sendo utilizadas como amostras para o classificador. Já existem alguns trabalhos que também se focam nesta metodologia. Esta tese foca-se num método que divide o texto em várias partes e trata cada uma como um grafo. Deste, são extraídas as medidas, que são tratadas como uma série temporal, da qual são extraídos momentos. Assim, os momentos compõem o vetor final, representativo de todo o texto. A partir da metodologia aqui descrita surgem mais duas variações. A primeira variação omite o passo das séries temporais, e, por consequência, as várias medidas de cada grafo são utilizadas diretamente como amostras. A segunda variação representa todo o texto como um só grafo. As metodologias são testadas com corpus em Inglês e Português, com número variado de textos; Abstract: Authorship Attribution using Co-Occurrence Networks This thesis approaches the task of Authorship Attribution as a classification task. This is done using methodologies that represent text documents in graphs, from which several measures are extracted, to be used as samples for the classifier. There have been some works that also focus on this methodology. This thesis focuses on a methodology which splits the texts in multiple parts and treats each as a separate graph, from which measures are extracted. Each graph’s measures are treated as a time-series and moments are extracted. These moments make the final vector, representative of the entire text. This methodology is explored and extended with 2 variations. The first variation skips the time-series step, resulting in the various measures from each graph being used directly as samples. The second variation models the entire text as one graph. The methodologies are tested in corpus in both English and Portuguese, with varying number of texts.
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Xi, Wenna. "Community Structure in Co-Location Networks." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1566156023255678.

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Kalluru, Vikram Gajanan. "Identify Condition Specific Gene Co-expression Networks." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338304258.

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Delgado, Román María del Carmen. "Organisation-based co-ordination of wireless sensor networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285080.

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Esta tesis presenta el Coalition Oriented Sensing Algorithm (COSA) como un mecanismo de auto-organización para redes de sensores inalámbricos (WSN). El objetivo del algoritmo es extender la vida útil de la red, al mismo tiempo que la funcionalidad básica de la misma – la monitorización fiel del entorno– también es garantizada. La evaluación del funcionamiento del algoritmo se apoya en una plataforma de simulación novedosa, RepastSNS. La implementación de COSA y la preparación de la plataforma para el desarrollo de los experimentos dan lugar a una estructura software reutilizable. Esta estructura favorece la implementación de futuras ampliaciones del algoritmo, así como su exportación a otros entornos. El uso de las WSNs se ha popularizado en los últimos años. Sus particulares características han favorecido la aplicación de las mismas a múltiples áreas. No obstante, la gestión energética de las WSNs sigue siendo objeto de estudio para los investigadores, que tratan de aliviar las fuertes restricciones que estas presentan en cuanto a disponibilidad de energía se refiere. En esta línea, se han propuesto diversas técnicas para conservación de la energía. La restricción energética es especialmente acusada cuando las WSNs se despliegan en entornos que no permiten la conexión de los nodos a la red ni la recarga de sus baterías. Este es el tipo de entorno considerado para la evaluación de COSA. El caso de uso estudiado considera una WSN desplegada a lo largo de un río navegable con el objetivo de monitorizar el estado del agua y detectar la presencia de polución en ella. La definición de COSA se inspira en el paradigma de los Sistemas Multiagente (MAS) mediante la identificación de los nodos de la WSN con agentes del MAS. COSA define un algoritmo para formación de coaliciones basado en diálogos por parejas de agentes (nodos). El algoritmo está completamente embebido en el comportamiento del agente. Los agentes que implementan COSA se comunican con sus vecinos para intercambiar información sobre su percepción del entorno y su estado. Como resultado de esta comunicación local, los agentes eligen su rol en la organización y establecen relaciones leader-follower. La definición de este tipo de relaciones se basa en dos funciones relacionales y un protocolo de negociación que establece las normas de coordinación. Los agentes se juntan en grupos para compensar la calidad de los datos recogidos y el consumo de energía asociado. Esta habilidad permite adaptar el consumo energético de la red a cambios en el entorno, al mismo tiempo que se satisfacen los objetivos de muestreo en cuanto a calidad de la información enviada al sink se refiere. Los resultados experimentales obtenidos apoyan las hipótesis preliminares en cuanto al comportamiento de COSA. A partir de estos resultados también se pone de manifiesto la relación existente entre la coordinación local y las ganancias obtenidas por el uso de COSA.
This thesis introduces the Coalition Oriented Sensing Algorithm (COSA) as a self-organisation mechanism for Wireless Sensor Networks (WSNs). This algorithm aims at extending the network lifetime at the same time that the primary goal of the network –faithfully monitoring the environment– is also guaranteed. The evaluation of the algorithm performance is based on a novel simulator, RepastSNS. The implementation of COSA and the development of its experimental setup define a reusable software structure to work over this simulation environment. It also favours the performance of future enhancements of the algorithm as well as its exportation. The use of WSNs has become widespread in the last years. The special characteristics of these networks have favoured their application to many different areas. One of the major concerns about WSNs refers to their energy management, as they are typically constraint in energy availability. This problem has gained the attention of researchers that try to improve this aspect of the WSNs by defining network energy conservation strategies. This constraint becomes especially acute when the network deployment environment does not allow for battery replenishment or node connection to the net. This is the case of the environment considered for COSA evaluation. The use case considered is a WSN deployed along a waterway in order to monitor the state of the water and detect the presence of pollutant sources. The definition of COSA is inspired by the Multiagent Systems (MAS) paradigm through the identification of nodes in a WSN with agents in a MAS. COSA defines a coalition formation algorithm based on peer-to-peer dialogues between neighbouring agents (nodes). The algorithm is completely embedded into the agent behaviour. Agents implementing COSA communicate with its neighbours to exchange information about their perception of the environment and their state. As a result of this local communication, agents select the role to play in the organisation and can then establish leader-follower relationships. The establishment of these peer-to-peer relationships is based on two relational functions and a negotiation protocol that lays down the norms of this co-ordination. Agents join in groups in order to trade off the accuracy of the sensed data and their energy consumption. As a consequence, COSA endows the network with self-organisation capacity. This ability is used to adapt energy consumption to changes in the environment and, at the same time, to fulfil sampling objectives in terms of the quality of the information reported to the sink. The results derived from experimentation support preliminary hypotheses about COSA good performance. They also provide insights on the relationship between local co-ordination and the gains obtained from COSA’s use.
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Hasan, Khondokar Fida. "GNSS time synchronisation in co-operative vehicular networks." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120849/1/Khondokar%20Fida_Hasan_Thesis.pdf.

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This thesis is a comprehensive study of time synchronisation issues in vehicular communication networks. It reviews the requirements of time synchronization in cooperative vehicular networks and examines the feasibility of Global Navigation Satellite System (GNSS) timing techniques for synchronising the networks. Results from experiments show that GNSS time synchronisation methods can replace existing time synchronisation function (TSF) based synchronisation in vehicular networks by offering high precision and high accuracy.
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Mutwil, Marek. "Integrative transcriptomic approaches to analyzing plant co-expression networks." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5075/.

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It is well documented that transcriptionally coordinated genes tend to be functionally related, and that such relationships may be conserved across different species, and even kingdoms. (Ihmels et al., 2004). Such relationships was initially utilized to reveal functional gene modules in yeast and mammals (Ihmels et al., 2004), and to explore orthologous gene functions between different species and kingdoms (Stuart et al., 2003; Bergmann et al., 2004). Model organisms, such as Arabidopsis, are readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer the acquired knowledge from these model organisms to species that are of greater importance to our society. However, due to large gene families in plants, the identification of functional equivalents of well characterized Arabidopsis genes in other plants is a non-trivial task, which often returns erroneous or inconclusive results. In this thesis, concepts of utilizing co-expression networks to help infer (i) gene function, (ii) organization of biological processes and (iii) knowledge transfer between species are introduced. An often overlooked fact by bioinformaticians is that a bioinformatic method is as useful as its accessibility. Therefore, majority of the work presented in this thesis was directed on developing freely available, user-friendly web-tools accessible for any biologist.
Es ist bereits ausgiebig gezeigt worden, dass Gene, deren Expression auf Transkriptionsebene koordiniert ist, häufig auch funktional in verwandten Stoffwechselwegen vorkommen, und dass sich dies wahrscheinlich auch Spezies- und sogar Reichübergreifend sagen lässt (Ihmels et al., 2004). Anfänglich wurden solche Beziehungen verwendet, um sogenannte Genfunktionsmodule in Hefe und Säugern aufzudecken (Ihmels et al., 2004), um dann orthologe Genfunktionen zwischen verschiedene Spezies und Reichen zu entdecken (Stuart et al., 2003; Bergmann et al., 2004). Modellorganismen wie Arabidopsis werden bevorzugt in der Forschung verwendet, weil man durch die schnelle Generationszeit in kurzer Zeit viele Daten erheben kann und aufgrund dessen die Ressourcen- und Informationsvielfalt um ein Vielfaches größer ist. Ein Hauptziel ist der Wissenstransfer von Modellorganismen auf Spezies, die gesellschaftlich von höherer Bedeutung sind wie z.B. Getreidearten oder andere Feldfrüchte. Pflanzen besitzen oft große Genfamilien und die eindeutige Identifizierung von gut charakterisierten Arabidopsisorthologen in besagten Nutzpflanzen ist kein triviales Vorhaben. In der vorliegenden Arbeit werden Konzepte zur Nutzung von Co-expressionsnetzwerken beschrieben, die helfen sollen (i) Genfunktionen zu identifizieren, (ii) die Organisation von biologischen Prozessen aufzuklären und (iii) das erworbene Wissen auf andere Spezies übertragbar zu machen. Ein häufig von Bioinformatikern übersehender Umstand ist, dass bioinformatische Methoden nur so sinnvoll sind wie ihre Zugänglichkeit. Deshalb basiert der Großteil dieser Arbeit auf freiverfügbaren und vor allem für Biologen nutzerfreundlichen Webtools.
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Ziebell, Sara E. "A Powerful Correlation Method for Microbial Co-Occurrence Networks." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/595812.

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Motivation: Network interpretation using correlations has several known difficulties. Firstly, the data structure has discrete counts with an excess of zeros creating non-normal non-continuous data. Secondly, correlations, often used as similarity measures in network inference, are not causal. Thirdly, there is a masking effect of mutualism on commensalism and competition on amensalism in ecological networks that interfere with interpretation (Faust and Raes, 2012). More explicitly, the symmetric nature of correlations (cor(X,Y)=cor(Y,X)) can mask the affect of the asymmetric ecology relationship (commensalism and amensalism). We aim to solve the third issue which may speed up targeted drug therapies or disease diagnosis based on specific relationships in gut microbiomes. Methods: We apply a non-symmetric correlation method, Gini Correlations which should serve as a better classifier of ecological relationships revealing a fuller picture of microbiomes. First, create simulated correlated and independent Zero-Inflated Negative Binomial data. Second, validate Gini correlations by comparing Gini with Pearson Spearman and Kendall correlations; calculate false positive rate, true positive rate, accuracy, ROC, AUC after applying Benjamini-Hochberg (1995) multiple testing correction. Simulation Result: Gini is consistent and out performs other methods for small sample sizes of 10 and 25 producing consistently low false positive rates across 64+ simulation settings as well as consistently high accuracy rates. When sample size is increased to 50 Gini performs as well as other methods. Real Data Result: For well-defined microbial communities Gini correlations found novel biologically and medically relevant relationships. However, Gini's ability to unmask non-symmetric ecological relationships is yet to be determined.
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Roth, Camille. "Co-evolution in epistemic networks : reconstructing social complex systems." Palaiseau, Ecole polytechnique, 2005. http://www.theses.fr/2005EPXX0057.

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Des agents produisant, manipulant et échangeant des connaissances constituent un système complexe socio-sémantique, dont l’étude représente un défi à la fois théorique, dans la perspective d’étendre la naturalisation des sciences sociales, et pratique, avec des applications permettant aux agents de connaître la dynamique du système dans lequel ils évoluent. Cette thèse se situe dans le cadre de ce programme de recherche. Parallèlement et plus largement, nous nous intéressons à la question de la reconstruction en sciences sociales. La reconstruction est un problème inverse comprenant deux volets complémentaires : (i) la déduction d’observations de haut-niveau à partir de phénomènes de bas-niveau ; et (ii) la reproduction de l’évolution des observations de haut-niveau à partir de la dynamique des objets de bas-niveau. Nous affirmons que plusieurs aspects significatifs de la structure d’une communauté de savoirs sont principalement produits par la dynamique d’un réseau épistémique où co-évoluent agents et concepts. En particulier, nous résolvons le premier volet du problème de la reconstruction en utilisant des treillis de Galois afin de recréer des taxonomies de communautés de savoirs à partir de simples relations entre agents et concepts; nous obtenons de fait une description historique se rapportant à la progression des champs, leur déclin, leur spécialisation ou leurs interactions (fusion ou scission). Nous micro-fondons ensuite la structure de ces communautés de savoirs en exhibant et en estimant empiriquement des processus d’interaction au niveau des agents, en co-évolution avec les concepts au sein du réseau épistémique, qui rendent compte de la morphogenèse et de l’émergence de plusieurs faits stylisés structurels de haut-niveau—il s’agit là du deuxième volet. Nous défendons finalement un point de vue épistémologique concernant la méthodologique générale de reconstruction d’un système complexe qui appuie notre choix d’un cadre coévolutionnaire
Agents producing and exchanging knowledge are forming as a whole a socio-semantic complex system. Studying such knowledge communities offers theoretical challenges, with the perspective of naturalizing further social sciences, as well as practical challenges, with potential applications enabling agents to know the dynamics of the system they are participating in. The present thesis lies within the framework of this research program. Alongside and more broadly, we address the question of reconstruction in social science. Reconstruction is a reverse problem consisting of two issues: (i) deduce a given high-level observation for a considered system from low-level phenomena; and (ii) reconstruct the evolution of high-level observations from the dynamics of lower-level objects. In this respect, we argue that several significant aspects of the structure of a knowledge community are primarily produced by the co-evolution between agents and concepts, i. E. The evolution of an epistemic network. In particular, we address the first reconstruction issue by using Galois lattices to rebuild taxonomies of knowledge communities from low-level observation of relationships between agents and concepts; achieving ultimately an historical description (inter alia field progress, decline, specialization, interaction - merging or splitting). We then micro-found various stylized facts regarding this particular structure, by exhibiting processes at the level of agents accounting for the emergence of epistemic community structure. After assessing the empirical interaction and growth processes, and assuming that agents and concepts are co-evolving, we successfully propose a morphogenesis model rebuilding relevant high-level stylized facts. We finally defend a general epistemological point related to the methodology of complex system reconstruction, eventually supporting our choice of a co-evolutionary framework
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Connor, Nora, Albert Barberán, and Aaron Clauset. "Using null models to infer microbial co-occurrence networks." PUBLIC LIBRARY SCIENCE, 2017. http://hdl.handle.net/10150/624498.

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Although microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.
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Books on the topic "Co-Networks"

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Patrickios, Costas S., ed. Amphiphilic Polymer Co-networks. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788015769.

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Poli, Federica. Co-operative Banking Networks in Europe. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21699-3.

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Johannessen, B. Gloria Guzmán, ed. Global Co-Mentoring Networks in Higher Education. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27508-6.

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Håkansson, Håkan. Corporate technological behaviour: Co-operation and networks. London: Routledge, 1989.

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Organisation for Economic Co-operation and Development., ed. Innovative networks: Co-operation in national innovation systems. Paris: OECD, 2001.

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European Commission. Industrial and Materials Technologies Programme., ed. Thematic networks activity: A road to European co-operation. Luxembourg: Office for Official Publications of the European Communities, 1999.

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Barry, Finbarr. European co-operation networks: Experiments in transnational urban & regional collaboration. Dublin: University College Dublin, 1993.

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Chundi, Pavan Kumar. Algorithm Hardware Co-Design of Neural Networks for Always-On Devices. [New York, N.Y.?]: [publisher not identified], 2021.

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S, Krishnan M., ed. The New Age of Innovation: Driving Co-Created Value Through Global Networks. New York: McGraw-Hill, 2008.

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Facebook und Co: Eine soziologische Analyse von Interaktionsformen in Online Social Networks. Wiesbaden: VS, Verl. fu r Sozialwiss., 2010.

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Book chapters on the topic "Co-Networks"

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Antoniou, Ioannis, Evangelos Ioannidis, and Nikos Varsakelis. "Co-evolutionary Complex Networks." In Nonequilibrium Thermodynamics and Fluctuation Kinetics, 277–302. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04458-8_14.

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Savić, Miloš, Mirjana Ivanović, and Lakhmi C. Jain. "Co-authorship Networks: An Introduction." In Intelligent Systems Reference Library, 179–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91196-0_5.

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Savić, Miloš, Mirjana Ivanović, and Lakhmi C. Jain. "Extraction of Co-authorship Networks." In Intelligent Systems Reference Library, 193–234. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91196-0_6.

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Savić, Miloš, Mirjana Ivanović, and Lakhmi C. Jain. "Analysis of Co-authorship Networks." In Intelligent Systems Reference Library, 235–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91196-0_7.

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Tayebi, Mohammad A., and Uwe Glässer. "Structure of Co-offending Networks." In Social Network Analysis in Predictive Policing, 15–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41492-8_3.

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Mali, Franc, Luka Kronegger, Patrick Doreian, and Anuška Ferligoj. "Dynamic Scientific Co-Authorship Networks." In Understanding Complex Systems, 195–232. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23068-4_6.

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Batten, David F. "Co-Evolutionary Learning on Networks." In Knowledge and Networks in a Dynamic Economy, 311–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-60318-1_18.

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Abhari, Kaveh, Bo Xiao, and Elizabeth Davidson. "Communication in Co-innovation Networks." In HCI in Business, Government and Organizations. Interacting with Information Systems, 139–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58481-2_12.

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Poli, Federica. "Co-operative Banking in Austria." In Co-operative Banking Networks in Europe, 97–145. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21699-3_3.

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Poli, Federica. "Co-operative Banking in Finland." In Co-operative Banking Networks in Europe, 147–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21699-3_4.

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Conference papers on the topic "Co-Networks"

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Meng, Zaiqiao, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. "Co-Embedding Attributed Networks." In WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3289600.3291015.

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Ruiqi Hu, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang, and Chengqi Zhang. "Co-clustering enterprise social networks." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727187.

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Ferry, J. P., and J. O. Bumgarner. "Tracking group co-membership on networks." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711940.

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Elhesha, Rasha, Aisharjya Sarkar, Christina Boucher, and Tamer Kahveci. "Identification of Co-evolving Temporal Networks." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3233547.3233686.

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Allen, Edward E., Anthony Pecorella, Jacquelyn S. Fetrow, David J. John, and William Turkett. "Reconstructing networks using co-temporal functions." In the 44th annual southeast regional conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1185448.1185541.

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Hailu, Sofonias, Alexis Dowhuszko, and Olav Tirkkonen. "Adaptive Co-primary Shared Access Between Co-located Radio Access Networks." In 9th International Conference on Cognitive Radio Oriented Wireless Networks. ICST, 2014. http://dx.doi.org/10.4108/icst.crowncom.2014.255381.

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Biuk-Aghai, Robert P. "Visualizing Co-Authorship Networks in Online Wikipedia." In 2006 International Symposium on Communications and Information Technologies. IEEE, 2006. http://dx.doi.org/10.1109/iscit.2006.339838.

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Collins, Travis F., and Alexander M. Wyglinski. "Co-Channel Interference in Future Femtocell Networks." In 2015 IEEE 82nd Vehicular Technology Conference (VTC Fall). IEEE, 2015. http://dx.doi.org/10.1109/vtcfall.2015.7390870.

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Palmieri, Stefania, and Daniela Amandolese. "UNIVERSITIES AND INDUSTRY: NETWORKS TO CO-INNOVATE." In International Technology, Education and Development Conference. IATED, 2016. http://dx.doi.org/10.21125/iceri.2016.0516.

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Tayebi, Mohammad A., and Uwe Glasser. "Organized Crime Structures in Co-offending Networks." In 2011 IEEE 9th International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 2011. http://dx.doi.org/10.1109/dasc.2011.144.

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Reports on the topic "Co-Networks"

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Battisti, Michele, Giovanni Peri, and Agnese Romiti. Dynamic Effects of Co-Ethnic Networks on Immigrants' Economic Success. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22389.

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Mamede, Ricardo. Labour Mobility, Industry Evolution and Social Networks: A Co-Evolutionary Model. DINÂMIA'CET-IUL, 2008. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2008.67.

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Hellerstein, Judith, Melissa McInerney, and David Neumark. Neighbors And Co-Workers: The Importance Of Residential Labor Market Networks. Cambridge, MA: National Bureau of Economic Research, July 2008. http://dx.doi.org/10.3386/w14201.

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Liu, Xiangqian. Co-channel Interference Mitigation for Robust Coexistence of Frequency Hopped Networks. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada482118.

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Kaminski, Linda, Magaly Lavadenz, Elvira Armas, and Grecya López. No. 11, November 2022: Insights from Co-Designed English Learner Improvement Networks. Center for Equity for English Learners, November 2022. http://dx.doi.org/10.15365/ceel.policy.12.

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This research brief presents a case study of an English Learner Improvement Network (ELIN), a group of educators focused on a shared problem of practice in English Learner education and supported through extensive collaboration between researchers and practitioners in English Learner education and Improvement Science. The case study involves an urban school district and a charter organization each serving between 50-80% of students who have ever been English Learners. The research brief identifies five key themes that contribute to knowledge of the English Learner Improvement Networks’ ability to support English Learner improvement: (1) Expert Partnerships Support Improvement; (2) Context Impacts Improvement; (3) Smaller Steps Lead to Larger Changes; (4) Collaborative Coaching Counts; and (5) Teacher Leaders Support Implementation. This ELIN is highlighted as a model of a systemic and coherent approach to educational improvement for ELs through the extensive collaboration provided in English Learner content and Improvement Science process.
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Costa, Dora, Matthew Kahn, Christopher Roudiez, and Sven Wilson. Persistent Social Networks: Civil War Veterans who Fought Together Co-Locate in Later Life. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22397.

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Aberman, Noora-Lisa, Loty Diop, and Roosmarijn Verstraeten. Analysis of nutrition research networks in West Africa: Application of social network analysis to co-authorship data to understand and enhance collaboration. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134587.

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Ozkan, Umit. Final Report for "Investigation of reaction networks and active sites in bio-ethanol steam reforming over Co-based catalysts" with all publications attached. Office of Scientific and Technical Information (OSTI), March 2011. http://dx.doi.org/10.2172/1010526.

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Ahlgren, Per, Tobias Jeppsson, Esa Stenberg, and Erik Berg. A bibliometric analysis of battery research with the BATTERY 2030+ roadmap as point of departure. Uppsala universitet, 2022. http://dx.doi.org/10.33063/diva-473454.

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In this bibliometric study, we analyze the six battery research subfields identified in the BATTERY 2030+ roadmap: Battery Interface Genome, Materials Acceleration Platform, Recyclability, Smart functionalities: Self-healing, Smart functionalities: Sensing, and Manufacturability. In addition, we analyze the entire research field related to BATTERY 2030+ as a whole, using two operationalizations. We (a) evaluate the European standing in the subfields/the BATTERY 2030+ field in comparison to the rest of the world, and (b) identify strongholds of the subfields/the BATTERY 2030+ field across Europe. For each subfield and the field as a whole, we used seed articles, i.e. articles listed in the BATTERY 2030+ roadmap or cited by such articles, in order to generate additional, similar articles located in an algorithmically obtained classification system. The output of the analysis is publication volumes, field normalized citation impact values with comparisons between country/country aggregates and between organizations, co-publishing networks between countries and organizations, and keyword co-occurrence networks. For the results related to (a), the performance of EU & associated (countries) is similar to China and the aggregate Japan-South Korea-Singapore and well below North America regarding citation impact and with respect to the field as a whole. Exceptions are, however, the subfields Battery Interface Genome and Recyclability. For the results related to (b), there is a large variability in the EU & associated organizations regarding volume in the different subfields. For citation impact, examples of high-performing EU & associated organizations are ETH Zurich and Max Planck Society for the Advancement of Science.
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Noack, Anika. Knowledge and Technology Transfer under Digital Conditions: Transfer Intermediaries in Eastern Germany and the Role of Digital Means, Trust and Face-to-Face Interactions. Technische Hochschule Wildau, 2022. http://dx.doi.org/10.15771/innohub_4.

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Even before the corona pandemic broke out in 2020, the role of digitalisation became more and more apparent within Knowledge and Technology Transfer (KTT) processes. Since the pandemic,intermediary organisations that bridge the distance between academia and the world of business to pave the way for successful university-industry linkages have not primarily been able to build on face-to-face-encounters to create those relations. Based on an ongoing research project, this paper examines how digitally mediated communications potentially enhance or limit knowledge and technology transfer that is primarily based on face-to-face interactions.On the one hand, the use of digitally mediated communications seem to foster the spatial expansion of networks, save travel times and costs and foster a special form of social inclusion. University-industry-relations, on the other hand,still rely on a positive evaluation of face-to-face contacts and geographical proximity for trust to develop between heterogeneous partners. Here, actors with bridging functions like transfer scouts are vital in enabling a regular communicative exchange to create commitment, social cohesion and cooperation in digital contexts. Although the relevance of digitalised transfer processes has been increasing over time, an important set of activities, involving face-to-face contacts and co-location, currently still plays a major role for transfer intermediaries in university-industry-relations.
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