Relevant bibliographies by topics / Behaviour network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Behaviour network'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Behaviour network"

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Turčaník, Michal. "Network User Behaviour Analysis by Machine Learning Methods." Information & Security: An International Journal 50 (2021): 66–78. http://dx.doi.org/10.11610/isij.5014.

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DRNOVSEK, MATEJA, OTMAR ZORN, and MARJANA MARTINCIC. "RESPONSIBLE ENTREPRENEURS: THE NETWORK EFFECTS." Journal of Enterprising Culture 16, no. 03 (September 2008): 209–31. http://dx.doi.org/10.1142/s0218495808000168.

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By drawing from the social network literature, this research analyses relationships between strong and weak ties and network's benefits for personal success of entrepreneurs. The empirical analysis is context-based within a specific network where such multiplex ties are embedded. In particular, networks of entrepreneurs who adhere to socially responsible behaviour through activities in the Rotary International Organisation are examined. Prior research into entrepreneurship has shown the varying benefits of strong and weak ties for personal success. The aim of this study is to develop a conceptual model of strong and weak ties related benefits and their contribution to personal success to empirically test it within a specific network context. We identified the Rotary International Organisation as such a network. The findings of this study indicate that engaging in socially responsible behaviours through organised attempts, such as the Rotary International network, brings significant benefits for the personal success of entrepreneurs. The implications of our empirical analysis indicate that the socially responsible behaviour of entrepreneurs can be motivated through network enhanced joint values such as adherence to high ethical standards in doing business and advancement of an international fellowship of business and professional people.
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BADHAM, JENNIFER, FRANK KEE, and RUTH F. HUNTER. "Simulating network intervention strategies: Implications for adoption of behaviour." Network Science 6, no. 2 (May 16, 2018): 265–80. http://dx.doi.org/10.1017/nws.2018.4.

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AbstractThis study uses simulation over real and artificial networks to compare the eventual adoption outcomes of network interventions, operationalized as idealized contagion processes with different sets of seeds. While the performance depends on the details of both the network and behaviour adoption mechanisms, interventions with seeds that are central to the network are more effective than random selection in the majority of simulations, with faster or more complete adoption throughout the network. These results provide additional theoretical justification for utilizing relevant network information in the design of public health behavior interventions.
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Ssali, Sarah, Glenn Wagner, Christopher Tumwine, Annette Nannungi, and Harold Green. "HIV Clients as Agents for Prevention: A Social Network Solution." AIDS Research and Treatment 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/815823.

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HIV prevention efforts to date have not explored the potential for persons living with HIV to act as change agents for prevention behaviour in their social networks. Using egocentric social network analysis, this study examined the prevalence and social network correlates of prevention advocacy behaviours (discussing HIV in general; encouraging abstinence or condom use, HIV testing, and seeking HIV care) enacted by 39 HIV clients in Uganda. Participants engaged in each prevention advocacy behaviour with roughly 50–70% of the members in their network. The strongest determinant of engaging in prevention advocacy with more of one’s network members was having a greater proportion of network members who knew one’s HIV seropositive status, as this was associated with three of the four advocacy behaviours. These findings highlight the potential for PLHA to be key change agents for HIV prevention within their networks and the importance of HIV disclosure in facilitating prevention advocacy.
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Hunt, P. J. "Pathological behaviour in loss networks." Journal of Applied Probability 32, no. 2 (June 1995): 519–33. http://dx.doi.org/10.2307/3215305.

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Hunt and Kurtz [9] consider a loss network as the number of circuits and the offered traffics become large. They prove a functional law of large numbers for such a network and illustrate their results with some simple examples. In this paper we apply their results to slightly more complicated examples to illustrate other, and sometimes surprising, behaviour of the loss networks in heavy traffic. The networks we consider operate under somewhat unusual routing rules but this is to enable us to produce the behaviour in networks with only a few links. In larger, real-world networks it is likely that much more ‘natural' and intuitively appealing routing rules could produce similar undesirable behaviour.
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Hunt, P. J. "Pathological behaviour in loss networks." Journal of Applied Probability 32, no. 02 (June 1995): 519–33. http://dx.doi.org/10.1017/s0021900200102955.

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Hunt and Kurtz [9] consider a loss network as the number of circuits and the offered traffics become large. They prove a functional law of large numbers for such a network and illustrate their results with some simple examples. In this paper we apply their results to slightly more complicated examples to illustrate other, and sometimes surprising, behaviour of the loss networks in heavy traffic. The networks we consider operate under somewhat unusual routing rules but this is to enable us to produce the behaviour in networks with only a few links. In larger, real-world networks it is likely that much more ‘natural' and intuitively appealing routing rules could produce similar undesirable behaviour.
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Ceni, Andrea, Peter Ashwin, and Lorenzo Livi. "Interpreting Recurrent Neural Networks Behaviour via Excitable Network Attractors." Cognitive Computation 12, no. 2 (March 23, 2019): 330–56. http://dx.doi.org/10.1007/s12559-019-09634-2.

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Zungeru, Adamu Murtala, Li-Minn Ang, and Kah Phooi Seng. "Termite-Hill." International Journal of Swarm Intelligence Research 3, no. 4 (October 2012): 1–22. http://dx.doi.org/10.4018/jsir.2012100101.

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Termites present a good natural metaphor to evolutionary computation. While each individual’s computational power is small compared to more evolved species, it is the power of their colonies that inspires communication engineers. This paper presents a study of artificial termites in sensor networks for the purpose of solving its’ routing problem. The behaviours of each of the termites in their colony allow their simulation in a restricted environment. The simulating behaviour demonstrates how the termites make use of an autocatalytic behaviour to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-hill demonstrates the principle of termites’ behavior to routing problem solving in the real applications of sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results as compared with other routing algorithms and with varying network density show that Termite-hill is scalable and improved on network energy consumption with a control over best-effort-service.
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Santos, Carmen Rodrguez. "CONSUMER BEHAVIOUR ERASMUS NETWORK - COBEREN." International Journal of Sales, Retailing and Marketing 1, no. 4 (January 1, 2012): 61–75. http://dx.doi.org/10.5848/apbj.2012.0037.

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Copello, Alex, Jim Orford, Ray Hodgson, Gillian Tober, and Clive Barrett. "Social behaviour and network therapy." Addictive Behaviors 27, no. 3 (May 2002): 345–66. http://dx.doi.org/10.1016/s0306-4603(01)00176-9.

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Dissertations / Theses on the topic "Behaviour network"

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Haschke, Robert. "Bifurcations in discrete time neural networks : controlling complex network behaviour with inputs." kostenfrei, 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973184663.

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Kulkarni, Shrinivas Bhalachandra. "The simulation studies on a behaviour based trust routing protocol for ad hoc networks." Diss., Online access via UMI:, 2006.

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Brierley, Matthew Joseph. "Neural network underlying snail feeding." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239132.

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James, Laura Bryony. "Error behaviour in optical networks." Thesis, University of Cambridge, 2005. https://www.repository.cam.ac.uk/handle/1810/265632.

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Optical fibre communications are now widely used in many applications, including local area computer networks. I postulate that many future optical LANs will be required to operate with limited optical power budgets for a variety of reasons, including increased system complexity and link speed, low cost components and minimal increases in transmit power. Some developers will wish to run links with reduced power budget margins, and the received data in these systems will be more susceptible to errors than has been the case previously. The errors observed in optical systems are investigated using the particular case of Gigabit Ethernet on fibre as an example. Gigabit Ethernet is one of three popular optical local area interconnects which use 8B/10B line coding, along with Fibre Channel and Infiniband, and is widely deployed. This line encoding is also used by packet switched optical LANs currently under development. A probabilistic analysis follows the effects of a single channel error in a frame, through the line coding scheme and the MAC layer frame error detection mechanisms. Empirical data is used to enhance this original analysis, making it directly relevant to deployed systems. Experiments using Gigabit Ethernet on fibre with reduced power levels at the receiver to simulate the effect of limited power margins are described. It is found that channel bit error rate and packet loss rate have only a weakly deterministic relationship, due to interactions between a number of non-uniform error characteristics at various network sub-layers. Some data payloads suffer from high bit error rates and low packet loss rates, compared to others with lower bit error rates and yet higher packet losses. Experiments using real Internet traffic contribute to the development of a novel model linking packet loss, the payload damage rate, and channel bit error rate. The observed error behaviours at various points in the physical and data link layers are detailed. These include data-dependent channel errors; this error hot- spotting is in contrast to the failure modes observed in a copper-based system. It is also found that both multiple channel errors within a single code-group, and multiple error instances within a frame, occur more frequently than might be expected. The overall effects of these error characteristics on the ability of cyclic redundancy checks (CRCs) to detect errors, and on the performance of higher layers in the network, is considered. This dissertation contributes to the discussion of layer interactions, which may lead to un-foreseen performance issues at higher levels of the network stack, and extends it by considering the physical and data link layers for a common form of optical link. The increased risk of errors in future optical networks, and my findings for 8B/10B encoded optical links, demonstrate the need for a cross-layer understanding of error characteristics in such systems. The development of these new networks should take error performance into account in light of the particular requirements of the application in question.
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Jacoby, David. "A network analysis approach to understanding shark behaviour." Thesis, University of Exeter, 2012. http://hdl.handle.net/10036/4093.

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The mechanisms and functions of shark grouping behaviour have received relatively little scientific attention to date. The current widespread use of social network analysis to study animal groups, in concert with rapid advances in animal tracking technology, now allows us to test specific hypotheses about how and why sharks form groups. This thesis uses replicated laboratory experiments to investigate some of the mechanisms underpinning aggregation in a model species of benthic, oviporous elasmobranch, the small spotted catshark (Scyliorhinus canicula L. 1758; Scyliorhinidae). Acoustic tracking of this species in the wild is also conducted to explore how network analyses can be adopted to study the localized movements, habitat connectivity and ranging behaviour of adult sharks. Groups of juvenile S. canicula were characterized by non-random social preferences, crucially, only when individuals were familiar with one another suggesting social recognition is important in young sharks of this species. Genetic analyses of parent and offspring DNA revealed very high levels of multiple paternity in this species, likely due to male sexual harassment and multiple mating, which leads to increased genetic diversity between juvenile sharks. Perhaps unsurprisingly, there was no evidence of kin relatedness structuring social interactions between conspecifics. Furthermore, testing the effects of environment on social behaviour provided evidence that these juveniles aggregated more in structurally complex environments than simple ones. However, at the individual level sharks showed consistency in their social network positions through time and across different habitat types. This result is indicative of personality traits in S. canicula. Using data gathered via passive acoustic telemetry of wild shark behaviour, network analysis provided a useful tool with which to quantify movement between receivers. One chapter has been dedicated to the application of these methods, highlighting a number of different analyses for predicting movement behaviour from such data. Finally, these methods were adopted to address ecological questions in this sexually segregated species. Persistent site fidelity to a localised inshore area by both male and female sharks suggested that segregation occurred at a relatively small spatial and temporal scale. Despite strong evidence of segregation, analyses of movement networks and individual co-occurrences revealed distinct periods of behavioural synchronicity during the months of March, April and May. In addition, habitat complexity appeared to be a significant driver of female behavioural strategy. Enhancing our knowledge of the social and environmental drivers of aggregation and movement in sharks is of great importance given the ecological threat facing many of our ocean’s top elasmobranch predators.
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Chan, Yun-sang Elvis, and 陳潤生. "Understanding of Chinese buying behaviour: a network approach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31265571.

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Chan, Yun-sang Elvis. "Understanding of Chinese buying behaviour : a network approach /." [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1357047X.

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Breutel, Stephan Werner. "Analysing the behaviour of neural networks." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15943/.

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A new method is developed to determine a set of informative and refined interface assertions satisfied by functions that are represented by feed-forward neural networks. Neural networks have often been criticized for their low degree of comprehensibility.It is difficult to have confidence in software components if they have no clear and valid interface description. Precise and understandable interface assertions for a neural network based software component are required for safety critical applications and for theintegration into larger software systems. The interface assertions we are considering are of the form "e if the input x of the neural network is in a region (alpha symbol) of the input space then the output f(x) of the neural network will be in the region (beta symbol) of the output space "e and vice versa. We are interested in computing refined interface assertions, which can be viewed as the computation of the strongest pre- and postconditions a feed-forward neural network fulfills. Unions ofpolyhedra (polyhedra are the generalization of convex polygons in higher dimensional spaces) are well suited for describing arbitrary regions of higher dimensional vector spaces. Additionally, polyhedra are closed under affine transformations. Given a feed-forward neural network, our method produces an annotated neural network, where each layer is annotated with a set of valid linear inequality predicates. The main challenges for the computation of these assertions is to compute the solution of a non-linear optimization problem and the projection of a polyhedron onto a lower-dimensional subspace.
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Kempener, Rudolf T. M. "From Organisational Behaviour to Industrial Network Evolutions: Stimulating Sustainable Development of Bioenergy Networks in Emerging Economies." University of Sydney, 2008. http://hdl.handle.net/2123/3985.

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Doctor of Philosophy (PhD)
The aim of this thesis is to understand what drives the evolution of industrial networks and how such understanding can be used to stimulate sustainable development. A complex adaptive systems perspective has been adopted to analyse the complex interaction between organisational behaviour and industrial network evolution. This analysis has formed the basis for the development of a modelling approach that allows for quantitative exploration of how different organisational perceptions about current and future uncertainty affect their behaviour and therefore the network evolution. This analysis results in a set of potential evolutionary pathways for an industrial network and their associated performance in terms of sustainable development. Subsequently, this modelling approach has been used to explore the consequences of interventions in the network evolution and to identify robust interventions for stimulating sustainable development of industrial networks. The analysis, modelling approach and development of interventions has been developed in the context of a bioenergy network in the region of KwaZulu-Natal in South Africa. Industrial networks are an important aspect of today’s life and provide many goods and services to households and individuals all over the world. They consist of a large number of autonomous organisations, where some organisations contribute by transforming or transacting natural resources, such as oil, agricultural products or water, while other organisations contribute to networks by providing information or setting regulation or subsidies (local or national governments) or by influencing decision making processes of other organisations in networks (advocacy groups). Throughout the process from natural resource to product or service, industrial networks have important economic, environmental and social impacts on the socio-economic and biophysical systems in which they operate. The sum of complex interactions between organisations affects the rate in which natural resources are used, environmental impacts associated with transformation and transaction of resources and social impacts on local communities, regions or countries as a whole. The aim of this thesis is to understand how industrial networks evolve and how they can be stimulated towards sustainable development. The first question that has been addressed in this thesis is how to understand the complex interaction between organisational behaviour and industrial network evolution. Organisational behaviour is affected by many functional and implicit characteristics within the environment in which the organisation operates, while simultaneously the environment is a function of non-linear relationships between individual organisational actions and their consequences for both the function and structure of the network. This thesis has identified four different characteristics of industrial networks that affect organisational behaviour: 1) Functional characteristics 2) Implicit behavioural characteristics 3) Implicit relational characteristics 4) Implicit network characteristics. Functional characteristics are those characteristics that are formally recognised by all organisations within an industrial network and which affect their position within the network. Examples of functional characteristics are the price and quantity of resources available, the location and distance of organisations within a network, infrastructure availability or regulation. Implicit characteristics, on the other hand, are those characteristics that impact the decision making process of organisations, but which are not formally part of the network. From an organisational perspective, implicit characteristics are the rules, heuristics, norms and values that an organisation uses to determine its objectives, position and potential actions. Implicit relational characteristics, most importantly trust and loyalty, affect an organisations choice between potential partners and implicit network characteristics are those social norms and values that emerge through social embeddedness. Collectively, these functional and implicit characteristics and their interactions determine the outcome of organisational decisions and therefore the direction of the industrial network evolution. The complex interaction between these large numbers of characteristics requires quantitative models to explore how different network characteristics and different interactions result in different network evolutions. This thesis has developed an agent-based simulation model to explore industrial network evolutions. To represent the multi-scale complexity of industrial networks, the model consists of four scales. Each scale represents different processes that connect the functional and implicit characteristics of an industrial network to each other. The two basic scales represent the strategic actions of the organisations on the one hand and the industrial network function and structure on the other. The third scale represents the processes that take place within the mental models of organisations describing how they make sense of their environment and inform their strategic decision making process. The fourth scale represents the social embeddedness of organisations and how social processes create and destroy social institutions. The model has been developed such that it allows for exploring how changes in different network characteristics or processes affect the evolution of the network as a whole. The second question that has been addressed in this thesis is how to evaluate sustainable development of different evolutionary pathways of industrial networks. First of all, a systems approach has been adopted to explore the consequences of an industrial network to the larger socio-economic and biophysical system in which the network operates. Subsequently, a set of structural indicators has been proposed to evaluate the dynamic performance of industrial networks. These four structural indicators reflect the efficiency, effectiveness, resilience and adaptiveness of industrial networks. Efficiency and effectiveness relate to the operational features by which industrial networks provides a particular contribution to society. Resilience and adaptiveness relate to the system’s capacity to maintain or adapt its contribution to society while under stress of temporary shocks or permanent shifts, respectively. Finally, different multi-criteria decision analysis (MCDA) tools have been applied to provide a holistic evaluation of sustainable development of industrial networks. The third important question that is addressed in this thesis is how to systematically explore the potential evolutionary pathways of an industrial network, which has led to the development of agent-based scenario analysis. Agent-based scenario analysis systematically explores how industrial network evolutions might evolve depending on the perceptions of organisations towards the inherent uncertainty associated with strategic decision making in networks. The agent-based scenario analysis consists of two steps. Firstly, analysts develop a set of coherent context scenarios, which represents their view on the context in which an industrial network will operate within the future. For a bioenergy network, for example, this step results in a set of scenarios that each represent a coherent future of the socio-economic system in which the network might evolve. The second step is the development of a set of ‘agent scenarios’. Each agent-based scenario is based on a different ‘mental model’ employed by organisations within the network about how to deal with the inherent ambiguity of the future. The organisational perspective towards uncertainty is of major importance for the evolution of industrial networks, because it determines the innovative behaviour of organisations, the structure of the network and the direction in which the network evolves. One the one hand, organisations can ignore future ambiguity and base their actions on the environment that they can observe in their present state. On the other extreme, organisations can adopt a view that the future is inherently uncertain and in which they view social norms and values more important than functional characteristics to make sense of their environment. The mental models are differentiated according to two dimensions: 1) different mental representation of the world and 2) different cognitive processes that can be employed to inform strategic actions. Along these dimensions, different processes can be employed to make sense of the environment and to inform decision making. The thesis has shown that by systematically exploring the different perceptions possible, an adequate understanding of the different evolutionary pathways can be gained to inform the evaluation and development of interventions to stimulate sustainable development. The final part of this thesis has applied the analysis and methodology developed throughout this thesis to a bioenergy network in the province of Kwazulu-Natal in South Africa. The bioenergy network consists of a set of existing sugar mills with large quantities of bagasse, a biomass waste product, available. Bagasse is currently burned inefficiently to produce steam for the sugar mills, but can potentially be used for the production of green electricity, biodiesel, bioethanol or gelfuel. All of these products have important consequences for the region in terms of associated reductions in CO2 emissions, electrification of and/or energy provision for rural households and local economic development of the region. This thesis has modelled strategic decisions of the sugar mills, the existing electricity generator, potential independent energy producers, local and national governments and how their actions and interactions can lead to different evolutionary pathways of the bioenergy network. The agent-based scenario analysis has been used to explore how different perceptions of organisations can lead to different network evolutions. Finally, the model has been used to explore the consequences of two categories of interventions on stimulating sustainable development. The conclusions are that both categories of interventions, financial interventions by national government and the introduction of multi-criteria decision analysis (MCDA) tools to aid strategic decision making, can have both positive and negative effects on the network evolutions, depending on what ‘mental models’ are employed by organisations. Furthermore, there is no single intervention that outperforms the others in terms of stimulating both functional and structural features of sustainable development. The final conclusion is that instead of focusing on individual or collective targets, emphasis should be placed on the development of interventions that focus on evolutionary aspects of industrial networks rather than functional performance criteria. This thesis has also highlighted interesting research questions for future investigation. The methodology developed in this thesis is applied to a single case study, but there are still many questions concerning how different industrial networks might benefit from different organisational perceptions towards uncertainty. Furthermore, the role between the mental models and sustainable development requires further investigation, especially in the light of globalisation and the interconnectiveness of industrial networks in different countries and continents. Finally, this methodology has provided a platform for investigating how new technologies might be developed that anticipate needs of future generations. This thesis has provided a first and important step in developing a methodology that addresses the complex issues associated with sustainable development, benefiting both academics and practitioners that aim to stimulate sustainable development.
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Kempener, Ruud T. M. "From organisational behaviour to industrial network evolutions stimulating sustainable development of bioenergy networks in emerging economies /." Connect to full text, 2008. http://ses.library.usyd.edu.au/handle/2123/3985.

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Thesis (Ph. D.)--University of Sydney, 2008.
Includes graphs and tables. Title from title screen (viewed December 17, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Chemical and Biomolecular Engineering, Faculty of Engineering and Information Technologies. Includes bibliographical references. Also available in print form.
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Books on the topic "Behaviour network"

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Strategic behaviour in network industries: A multidisciplinary approach. Cheltenham, Glos, UK: Edward Elgar, 2009.

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Souloglou, Adonis. Marketing communication strategies and buyer behaviour: the hub segment of the European local area network market. Manchester: UMIST, 1995.

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Schreckenberg, Michael. Human Behaviour and Traffic Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.

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Schreckenberg, Michael, and Reinhard Selten, eds. Human Behaviour and Traffic Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07809-9.

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Omohundro, Stephen M. Efficient algorithms with neural network behavior. Urbana, Il (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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1972-, Ghirlanda Stefano, ed. Neural networks and animal behavior. Princeton: Princeton University Press, 2005.

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Josić, Kres̆imir. Coherent behavior in neuronal networks. New York: Springer, 2009.

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Selverston, Allen I., ed. Model Neural Networks and Behavior. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4757-5858-0.

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Josic, Kre¿imir, Jonathan Rubin, Manuel Matias, and Ranulfo Romo, eds. Coherent Behavior in Neuronal Networks. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0389-1.

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International Society for Invertebrate Neurobiology. Symposium. Neurobiology of invertebrates: Signal molecules, networks, behaviour. Budapest: Akadémiai Kiadó, 1993.

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Book chapters on the topic "Behaviour network"

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Ştefănescu, Gheorghe. "Network behaviour." In Network Algebra, 123–45. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0479-7_5.

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Treur, Jan. "Relating Emerging Network Behaviour to Network Structure." In Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models, 251–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31445-3_11.

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Treur, Jan. "Relating Emerging Network Behaviour to Network Structure." In Studies in Computational Intelligence, 619–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05411-3_50.

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Berninghaus, Siegfried K., and Bodo Vogt. "Network Formation and Co-ordination Games." In Advances in Understanding Strategic Behaviour, 55–72. London: Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9780230523371_4.

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Armbrust, Christopher, Thorsten Ropertz, Lisa Kiekbusch, and Karsten Berns. "Quantitative Aspects of Behaviour Network Verification." In Advances in Artificial Intelligence, 218–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_19.

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Akinalp, Coskun, and Herwig Unger. "Node Behaviour Driven Network Topology Adaption." In Autonomous Systems: Developments and Trends, 229–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24806-1_18.

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Andriatsimandefitra, Radoniaina, and Valérie Viet Triem Tong. "Capturing Android Malware Behaviour Using System Flow Graph." In Network and System Security, 534–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11698-3_43.

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Gorochowski, Thomas E., and Thomas O. Richardson. "How Behaviour and the Environment Influence Transmission in Mobile Groups." In Temporal Network Epidemiology, 17–42. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5287-3_2.

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Milligan, Derek K., and Manissa J. Dobrée Wilson. "Fundamental Structure/Behaviour Relationships in Synchronous Boolean Neural Networks." In International Neural Network Conference, 997–1000. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_164.

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Nijkamp, Peter, Gerard Pepping, and David Banister. "Car Drivers’ Response and Network Characteristics: An Italian Case Study." In Telematics and Transport Behaviour, 121–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80139-6_6.

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Conference papers on the topic "Behaviour network"

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Lu, Jun, Yu Wang, Zhongwang Wu, and Yu Lu. "Network Behaviour Description and Behaviour Base Modeling Method." In The Proceedings of the Multiconference on "Computational Engineering in Systems Applications". IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.313526.

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Lu, Jun, Yu Wang, Zhongwang Wu, and Yu Lu. "Network Behaviour Description and Behaviour Base Modeling Method." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281848.

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Deng, Jie, Gareth Tyson, Felix Cuadrado, and Steve Uhlig. "Keddah: Capturing Hadoop Network Behaviour." In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2017. http://dx.doi.org/10.1109/icdcs.2017.211.

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Choi, Sunoh, Yangseo Choi, Jooyoung Lee, Jonghyun Kim, and Ikkyun Kim. "Network abnormal behaviour analysis system." In 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890055.

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Luo, Jinshan, Atsushi Ito, Akira Sasaki, Madoka Hasegawa, Shiori Ashibe, Yoshikazu Nagao, Yuko Hiramatsu, Kotaro Torii, and Toru Aoki. "Sensor Network for Monitoring Livestock Behaviour." In 2020 IEEE SENSORS. IEEE, 2020. http://dx.doi.org/10.1109/sensors47125.2020.9278693.

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Czachorski, Tadeusz, Erol Gelenbe, Godlove Suila Kuaban, and Dariusz Marek. "Transient Behaviour of a Network Router." In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2020. http://dx.doi.org/10.1109/tsp49548.2020.9163477.

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Gilani, Zafar, Arjuna Sathiaseelan, Jon Crowcroft, and Veljko Pejovic. "Inferring network infrastructural behaviour during disasters." In 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2016. http://dx.doi.org/10.1109/ccnc.2016.7444855.

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Khan, Khadijah Saeed, and Eeva-Liisa Eskola. "The cultural landscape of women refugees in Sweden - a road to information and integration." In ISIC: the Information Behaviour Conference. University of Borås, Borås, Sweden, 2020. http://dx.doi.org/10.47989/irisic2033.

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Introduction. This research in progress explores women refugees’ information and integration challenges from the cultural perspective and proposes the concept of ‘cultural landscape’ as facilitator to refugees’ information and integration practices in Sweden. Method. A qualitative research method of participatory observation, semi-structured interviews and unofficial discussions as a complement is been used in this study. Analysis. The thematic analysis approach is used to analyse the observation and interviews data. Results. Participants describe how two different forms of cultural landscapes – ‘reading and learning circles’ and ‘doing and learning circles’ have helped them in reconstructing fractured information landscapes by building bridges into new communities, maintaining links with co-cultural community network and achieving a sense of belonging and identity by psychological and spiritual support. Conclusions. The research will identify the importance of cultural landscape in meeting refugees’ information and integration challenges in a new country.
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Zielinski, Bartlomiej. "IEEE 802.11 network behaviour in the presence of Bluetooth network." In 2007 Second International Conference on Systems and Networks Communications (ICSNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icsnc.2007.39.

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Tsompanidis, Ilias, Ahmed H. Zahran, and Cormac J. Sreenan. "Mobile network traffic: A user behaviour model." In 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC). IEEE, 2014. http://dx.doi.org/10.1109/wmnc.2014.6878862.

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Reports on the topic "Behaviour network"

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Schulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.

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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.
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Johnson, Joseph E., Vladimir Gudkov, Chin-Tser Huang, Cilia Farkas, and Duncan Buell. New Metrics for Characterizing and Predicting Network Behavior. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada462797.

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Kater, S. B., and Barbara C. Hayes. Circuit Behavior in the Development of Neuronal Networks. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada198040.

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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Teitel, S. Flux flow, pinning, and resistive behavior in superconducting networks. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/6048147.

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Teitel, S. Flux flow, pinning, and resistive behavior in superconducting networks. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/5241523.

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Teitel, Stephen. Flux Flow, Pinning, and Resistive Behavior in Superconducting Networks. Office of Scientific and Technical Information (OSTI), May 2005. http://dx.doi.org/10.2172/839348.

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Teitel, S. Flux flow pinning and resistive behavior in superconducting networks. Office of Scientific and Technical Information (OSTI), October 1990. http://dx.doi.org/10.2172/6504361.

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Teitel, S. Flux flow, pinning, and resistive behavior in superconducting networks. Office of Scientific and Technical Information (OSTI), October 1992. http://dx.doi.org/10.2172/6958865.

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Dauskardt, Reinhold. Mechanical Behavior of Hybrids with Hyper-Connected Molecular Networks. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1765147.

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