Literatura académica sobre el tema "Endogenous diffusion social networks"
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Artículos de revistas sobre el tema "Endogenous diffusion social networks"
Goldberg, Amir y Sarah K. Stein. "Beyond Social Contagion: Associative Diffusion and the Emergence of Cultural Variation". American Sociological Review 83, n.º 5 (14 de septiembre de 2018): 897–932. http://dx.doi.org/10.1177/0003122418797576.
Texto completoChang, Myong‐Hun y Joseph E. Harrington, Jr. "Discovery and Diffusion of Knowledge in an Endogenous Social Network". American Journal of Sociology 110, n.º 4 (enero de 2005): 937–76. http://dx.doi.org/10.1086/426555.
Texto completoPavan, Elena. "Embedding Digital Communications Within Collective Action Networks: A Multidimensional Network Approach". Mobilization: An International Quarterly 19, n.º 4 (1 de diciembre de 2014): 441–55. http://dx.doi.org/10.17813/maiq.19.4.w24rl524u074126k.
Texto completoHuang, Hung-Chun y Hsin-Yu Shih. "Exploring the structure of international technology diffusion". Foresight 16, n.º 3 (3 de junio de 2014): 210–28. http://dx.doi.org/10.1108/fs-11-2012-0085.
Texto completoKoley, Paramita, Avirup Saha, Sourangshu Bhattacharya, Niloy Ganguly y Abir De. "Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach". ACM Transactions on Knowledge Discovery from Data 15, n.º 6 (28 de junio de 2021): 1–25. http://dx.doi.org/10.1145/3449361.
Texto completoWejnert, Barbara. "Diffusion, Development, and Democracy, 1800-1999". American Sociological Review 70, n.º 1 (febrero de 2005): 53–81. http://dx.doi.org/10.1177/000312240507000104.
Texto completoIoannidis, Evangelos, Nikos Varsakelis y Ioannis Antoniou. "Promoters versus Adversaries of Change: Agent-Based Modeling of Organizational Conflict in Co-Evolving Networks". Mathematics 8, n.º 12 (17 de diciembre de 2020): 2235. http://dx.doi.org/10.3390/math8122235.
Texto completoXie, Xiaoyi y Peiji Shi. "Dynamic Evolution and Collaborative Development Model of Urban Agglomeration in Hexi Corridor from the Perspective of Economic Flow". Land 12, n.º 2 (18 de enero de 2023): 274. http://dx.doi.org/10.3390/land12020274.
Texto completoHojman, Daniel A. y Adam Szeidl. "Endogenous networks, social games, and evolution". Games and Economic Behavior 55, n.º 1 (abril de 2006): 112–30. http://dx.doi.org/10.1016/j.geb.2005.02.007.
Texto completoChristozov, Dimitar y Stefka Toleva-Stoimenova. "Knowledge Diffusion via Social Networks". International Journal of Digital Literacy and Digital Competence 4, n.º 2 (abril de 2013): 1–12. http://dx.doi.org/10.4018/jdldc.2013040101.
Texto completoTesis sobre el tema "Endogenous diffusion social networks"
MUSCILLO, ALESSIO. "Endogenous Diffusion in Social Networks. Two Cases: Infectious Diseases and Sharing of Knowledge". Doctoral thesis, Università di Siena, 2017. http://hdl.handle.net/11365/1059090.
Texto completoBimpikis, Kostas. "Strategic delay and information exchange in endogenous social networks". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62405.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (p. 160-165).
This thesis studies optimal stopping problems for strategic agents in the context of two economic applications: experimentation in a competitive market and information exchange in social networks. The economic agents (firms in the first application, individuals in the second) take actions, whose payoffs depend on an unknown underlying state. Our framework is characterized by the following key feature: agents time their actions to take advantage of either the outcome of the actions of others (experimentation model) or information obtained over time by their peers (information exchange model). Equilibria in both environments are typically inefficient, since information is imperfect and, thus, there is a benefit in being a late mover, but delaying is costly. More specifically, in the first part of the thesis, we develop a model of experimentation and innovation in a competitive multi-firm environment. Each firm receives a private signal on the success probability of a research project and decides when and which project to implement. A successful innovation can be copied by other firms. We start the analysis by considering the symmetric environment, where the signal quality is the same for all firms. Symmetric equilibria (where actions do not depend on the identity of the firm) always involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous rather than staggered experimentation. The social cost of insufficient experimentation can be arbitrarily large. Then, we study the role of simple instruments in improving over equilibrium outcomes. We show that appropriately-designed patents can implement the socially optimal allocation (in all equilibria) by encouraging rapid experimentation and efficient ex post transfer of knowledge across firms. In contrast to patents, subsidies to experimentation, research, or innovation cannot typically achieve this objective. We also discuss the case when signal quality is private information and differs across firms. We show that in this more general environment patents again encourage experimentation and reduce delays. In the second part, we study a model of information exchange among rational individuals through communication and investigate its implications for information aggregation in large societies. An underlying state (of the world) determines which action has higher payoff. Agents receive a private signal correlated with the underlying state. They then exchange information over their social network until taking an (irreversible) action. We define asymptotic learning as the fraction of agents taking an action that is close to optimal converging to one in probability as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the social network most agents are a short distance away from "information hubs", which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication is not always optimal, when the communication network induces asymptotic learning (in a large society), truthful communication is an equilibrium. Then, we discuss the welfare implications of equilibrium behavior. In particular, we compare the aggregate welfare at equilibrium with that of the optimal allocation, which is defined as the strategy profile a social planner would choose, so as to maximize the expected aggregate welfare. We show that when asymptotic learning occurs all equilibria are efficient. A partial converse is also true: if asymptotic learning does not occur at the optimal allocation and an additional mild condition holds at an equilibrium, then the equilibrium is inefficient. Furthermore, we discuss how our learning results can be applied to several commonly studied random graph models, such as preferential attachment and Erdos-Renyi graphs. In the final part, we study strategic network formation in the context of information exchange. In particular, we relax the assumption that the social network over which agents communicate is fixed, and we let agents decide which agents to form a communication link with incurring an associated cost. We provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results if social cliques are neither too numerous nor too large, in which case communication across cliques is encouraged.
by Kostas Bimpikis.
Ph.D.
Pedersen, Tavis Joseph. "Tracking infection diffusion in social networks". Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62557.
Texto completoApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Sun, Hongxian y 孙鸿賢. "Modeling information diffusion in social networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48330127.
Texto completopublished_or_final_version
Computer Science
Master
Master of Philosophy
DE, NICOLA ANTONIO. "Diffusion of interests in social networks". Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/202331.
Texto completoYang, Yile y 楊頤樂. "Noncooperative information diffusion in online social networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206693.
Texto completopublished_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
Marchenko, Maria. "Endogenous Shocks in Social Networks: Exam Failures and Friends' Future Performance". WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7100/1/wp292.pdf.
Texto completoSeries: Department of Economics Working Paper Series
Louzada, Pinto Julio Cesar. "Information diffusion and opinion dynamics in social networks". Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0001/document.
Texto completoOur aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics
Alemayehu, Atsede Ghidey <1986>. "Essays on social networks, altruism and information diffusion". Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9873/1/Atsede%20Ghidey%20Alemayehu_PhD%20Dissertation%282021%29%20Social%20Networks%20Altruism%20and%20Information%20Diffusion-final%20version.pdf.
Texto completoMarchenko, Maria. "Dealing with Endogenous Shocks in Dynamic Friendship Network". WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/7099/1/wp291.pdf.
Texto completoSeries: Department of Economics Working Paper Series
Libros sobre el tema "Endogenous diffusion social networks"
Shakarian, Paulo, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani y Ruocheng Guo. Diffusion in Social Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23105-1.
Texto completoKlochko, Marianna A. Endogenous time preferences in social networks. Cheltenham, UK: Edward Elgar Pub., 2005.
Buscar texto completo1942-, Ordeshook Peter C., ed. Endogenous time preferences in social networks. Northhampton, MA: Edward Elgar Pub., 2006.
Buscar texto completoAcemoglu, Daron. Dynamics of information exchange in endogenous social networks. Cambridge, MA: National Bureau of Economic Research, 2010.
Buscar texto completoAcemoglu, Daron. Dynamics of information exchange in endogenous social networks. Cambridge, MA: National Bureau of Economic Research, 2010.
Buscar texto completoWindzio, Michael, Ivo Mossig, Fabian Besche-Truthe y Helen Seitzer, eds. Networks and Geographies of Global Social Policy Diffusion. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83403-6.
Texto completoAssimilating identities: Social networks and the diffusion of sections. [Sydney?]: University of Sydney, 2005.
Buscar texto completo1965-, Coutard Olivier, Hanley Richard y Zimmerman Rae, eds. Sustaining urban networks: The social diffusion of large technical systems. London: Routledge, 2004.
Buscar texto completoSocial networks, innovation and the knowledge economy. New York: Routledge, 2012.
Buscar texto completoWang, Haiyan, Feng Wang y Kuai Xu. Modeling Information Diffusion in Online Social Networks with Partial Differential Equations. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38852-2.
Texto completoCapítulos de libros sobre el tema "Endogenous diffusion social networks"
Aggrawal, Niyati y Adarsh Anand. "Information Diffusion". En Social Networks, 173–90. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-10.
Texto completoDuggan, Jim. "Diffusion Models". En Lecture Notes in Social Networks, 97–121. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34043-2_5.
Texto completoLouni, Alireza y K. P. Subbalakshmi. "Diffusion of Information in Social Networks". En Social Networking, 1–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05164-2_1.
Texto completoImmorlica, Nicole. "Technology Diffusion in Social Networks". En Lecture Notes in Computer Science, 35–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95891-8_5.
Texto completoEtesami, Seyed Rasoul. "Diffusion Games over Social Networks". En Potential-Based Analysis of Social, Communication, and Distributed Networks, 135–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54289-8_7.
Texto completoXu, Wen, Weili Wu, Lidan Fan, Zaixin Lu y Ding-Zhu Du. "Influence Diffusion in Social Networks". En Optimization in Science and Engineering, 567–81. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0808-0_27.
Texto completoAl-Taie, Mohammed Zuhair y Seifedine Kadry. "Information Diffusion in Social Networks". En Advanced Information and Knowledge Processing, 165–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53004-8_8.
Texto completoValente, Thomas W. "Social Networks, Diffusion Processes in". En Computational Complexity, 2940–52. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_181.
Texto completoValente, Thomas W. "Social Networks, Diffusion Processes in". En Encyclopedia of Complexity and Systems Science, 8306–19. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_493.
Texto completoBojic, Iva, Tomislav Lipic y Vedran Podobnik. "Bio-inspired Clustering and Data Diffusion in Machine Social Networks". En Computational Social Networks, 51–79. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_3.
Texto completoActas de conferencias sobre el tema "Endogenous diffusion social networks"
De, Abir, Sourangshu Bhattacharya y Niloy Ganguly. "Demarcating Endogenous and Exogenous Opinion Diffusion Process on Social Networks". En the 2018 World Wide Web Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186121.
Texto completoBimpikis, Kostas, Daron Acemoglu y Asuman Ozdaglar. "Communication dynamics in endogenous social networks". En the Behavioral and Quantitative Game Theory. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807406.1807499.
Texto completoTatara, Eric, Nicholson Collier, Jonathan Ozik y Charles Macal. "Endogenous Social Networks from Large-Scale Agent-Based Models". En 2017 IEEE International Parallel and Distributed Processing Symposium: Workshops (IPDPSW). IEEE, 2017. http://dx.doi.org/10.1109/ipdpsw.2017.83.
Texto completoChanhyun Kang, C. Molinaro, S. Kraus, Y. Shavitt y V. S. Subrahmanian. "Diffusion Centrality in Social Networks". En 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.95.
Texto completoAgrawal, Divyakant, Ceren Budak y Amr El Abbadi. "Information diffusion in social networks". En the 20th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2064036.
Texto completoNeves, Felipe, Victor Ströele y Fernanda Campos. "Information Diffusion in Social Networks". En SBSI'19: XV Brazilian Symposium on Information Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3330204.3330234.
Texto completoMehdiabadi, Motahareh Eslami, Hamid R. Rabiee y Mostafa Salehi. "Sampling from Diffusion Networks". En 2012 International Conference on Social Informatics (SocialInformatics). IEEE, 2012. http://dx.doi.org/10.1109/socialinformatics.2012.79.
Texto completoBalali, Ali, Aboozar Rajabi, Sepehr Ghassemi, Masoud Asadpour y Hesham Faili. "Content diffusion prediction in social networks". En 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE, 2013. http://dx.doi.org/10.1109/ikt.2013.6620114.
Texto completoGayraud, Nathalie T. H., Evaggelia Pitoura y Panayiotis Tsaparas. "Diffusion Maximization in Evolving Social Networks". En COSN'15: Conference on Online Social Networks. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2817946.2817965.
Texto completoAmato, Flora, Vincenzo Moscato, Antonio Picariello y Giancarlo Sperlí. "Diffusion Algorithms in Multimedia Social Networks". En ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3116207.
Texto completoInformes sobre el tema "Endogenous diffusion social networks"
Acemoglu, Daron, Kostas Bimpikis y Asuman Ozdaglar. Dynamics of Information Exchange in Endogenous Social Networks. Cambridge, MA: National Bureau of Economic Research, septiembre de 2010. http://dx.doi.org/10.3386/w16410.
Texto completoMontgomery, Mark y John Casterline. Social networks and the diffusion of fertility control. Population Council, 1998. http://dx.doi.org/10.31899/pgy6.1020.
Texto completoHirshleifer, David, Lin Peng y Qiguang Wang. News Diffusion in Social Networks and Stock Market Reactions. Cambridge, MA: National Bureau of Economic Research, enero de 2023. http://dx.doi.org/10.3386/w30860.
Texto completoHalberstam, Yosh y Brian Knight. Homophily, Group Size, and the Diffusion of Political Information in Social Networks: Evidence from Twitter. Cambridge, MA: National Bureau of Economic Research, noviembre de 2014. http://dx.doi.org/10.3386/w20681.
Texto completoRose, Erin M. y Beth A. Hawkins. Assessing the Potential of Social Networks as a Means for Information Diffusion the Weatherization Experiences (WE) Project. Office of Scientific and Technical Information (OSTI), abril de 2015. http://dx.doi.org/10.2172/1354643.
Texto completoDillon, Andrew, Deanna Olney, Marie Ruel, Fanny Yago-Wienne, Jennifer Nielsen, Marcellin Ouedraogo, Abdoulaye Pedehombga, Hippolyte Rouamba y Olivier Vebamba. The diffusion of health knowledge through social networks: An impact evaluation of health knowledge asymmetries on child health in Burkina Faso. International Initiative for Impact Evaluation, 2014. http://dx.doi.org/10.23846/ow2170.
Texto completoBehrman, Jere R., Hans-Peter Kohler y Susan Cotts Watkins. How can we measure the causal effects of social networks using observational data? Evidence from the diffusion of family planning and AIDS worries in South Nyanza District, Kenya. Rostock: Max Planck Institute for Demographic Research, julio de 2001. http://dx.doi.org/10.4054/mpidr-wp-2001-022.
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