Literatura académica sobre el tema "Computational social networks"
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Artículos de revistas sobre el tema "Computational social networks"
Nasution, Mahyuddin K. M., Rahmad Syah y Marischa Elveny. "Social Network Analysis: Towards Complexity Problem". Webology 18, n.º 2 (23 de diciembre de 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.
Texto completoPenn, A. "Synthetic networks — Spatial, social, structural and computational". BT Technology Journal 24, n.º 3 (julio de 2006): 49–56. http://dx.doi.org/10.1007/s10550-006-0075-0.
Texto completoWu, Jia, Fangfang Gou, Wangping Xiong y Xian Zhou. "A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks". Complexity 2021 (26 de noviembre de 2021): 1–16. http://dx.doi.org/10.1155/2021/8554351.
Texto completoNwanga, E. M., K. C. Okafor, G. A. Chukwudebe y I. E. Achumba. "Computational Robotics: An Alternative Approach for Predicting Terrorist Networks". International Journal of Robotics and Automation Technology 8 (24 de noviembre de 2021): 1–11. http://dx.doi.org/10.31875/2409-9694.2021.08.1.
Texto completoAtdag, Samet y Haluk O. Bingol. "Computational models for commercial advertisements in social networks". Physica A: Statistical Mechanics and its Applications 572 (junio de 2021): 125916. http://dx.doi.org/10.1016/j.physa.2021.125916.
Texto completoIsmaili, Anisse y Patrice Perny. "Computational social choice for coordination in agent networks". Annals of Mathematics and Artificial Intelligence 77, n.º 3-4 (13 de junio de 2015): 335–59. http://dx.doi.org/10.1007/s10472-015-9462-x.
Texto completoTomassini, Marco y Alberto Antonioni. "Computational Behavioral Models for Public Goods Games on Social Networks". Games 10, n.º 3 (2 de septiembre de 2019): 35. http://dx.doi.org/10.3390/g10030035.
Texto completoLi, Wei y Sisi Zlatanova. "Significant Geo-Social Group Discovery over Location-Based Social Network". Sensors 21, n.º 13 (2 de julio de 2021): 4551. http://dx.doi.org/10.3390/s21134551.
Texto completoYan, Yeqing, Zhigang Chen, Jia Wu y Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks". Algorithms 11, n.º 8 (14 de agosto de 2018): 125. http://dx.doi.org/10.3390/a11080125.
Texto completoWang, Pingshui, Jianwen Zhu y Qinjuan Ma. "Private Data Protection in Social Networks Based on Blockchain". International Journal of Advanced Networking and Applications 14, n.º 04 (2023): 5549–55. http://dx.doi.org/10.35444/ijana.2023.14407.
Texto completoTesis sobre el tema "Computational social networks"
Hamdi, Sana. "Computational models of trust and reputation in online social networks". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL001/document.
Texto completoOnline Social Networks (OSNs) have known a dramatic increase and they have been used as means for a rich variety of activities. In fact, within OSNs, usersare able to discover, extend, manage, and leverage their experiences and opinionsonline. However, the open and decentralized nature of the OSNs makes themvulnerable to the appearance of malicious users. Therefore, prospective users facemany problems related to trust. Thus, effective and efficient trust evaluation isvery crucial for users’ decision-making. It provides valuable information to OSNsusers, enabling them to make difference between trustworthy and untrustworthyones. This thesis aims to provide effective and efficient trust and reputationmanagement methods to evaluate trust and reputation of OSNs users, which canbe divided into the following four contributions.The first contribution presents a complex trust-oriented users’ contexts andinterests extraction, where the complex social contextual information is taken intoaccount in modelling, better reflecting the social networks in reality. In addition,we propose an enrichment of the Dbpedia ontology from conceptualizations offolksonomies.We second propose the IRIS (Interactions, Relationship types and Interest Similarity)trust management approach allowing the generation of the trust networkand the computation of direct trust. This model considers social activities of usersincluding their social relationships, preferences and interactions. The intentionhere is to form a solid basis for the reputation and indirect trust models.The third contribution of this thesis is trust inference in OSNs. In fact, it isnecessary and significant to evaluate the trust between two participants whomhave not direct interactions. We propose a trust inference model called TISON(Trust Inference in Social Networks) to evaluate Trust Inference within OSNs.The fourth contribution of this thesis consists on the reputation managementin OSNs. To manage reputation, we proposed two new algorithms. We introducea new exclusive algorithm for clustering users based on reputation, called RepC,based on trust network. In addition, we propose a second algorithm, FCR, whichis a fuzzy extension of RepC.For the proposed approaches, extensive experiments have been conducted onreal or random datasets. The experimental results have demonstrated that ourproposed algorithms generate better results, in terms of the utility of delivered results and efficiency, than do the pioneering approaches of the literature
Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science". Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.
Texto completoLa presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.
Mui, Lik. "Computational models of trust and reputation : agents, evolutionary games, and social networks". Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87343.
Texto completoIncludes bibliographical references (leaves [131]-139).
Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation.
(cont.) We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to.
by Lik Mui.
Ph.D.
Yang, Guoli. "Learning in adaptive networks : analytical and computational approaches". Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20956.
Texto completoKuhlman, Christopher J. "High Performance Computational Social Science Modeling of Networked Populations". Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/51175.
Texto completoPh. D.
Khan, Pour Hamed. "Computational Approaches for Analyzing Social Support in Online Health Communities". Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157594/.
Texto completoRossi, Maria. "Graph Mining for Influence Maximization in Social Networks". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX083/document.
Texto completoModern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures
Shahrezaye, Morteza [Verfasser], Simon [Akademischer Betreuer] Hegelich, Jürgen [Gutachter] Pfeffer y Simon [Gutachter] Hegelich. "Understanding big social networks: Applied methods for computational social science / Morteza Shahrezaye ; Gutachter: Jürgen Pfeffer, Simon Hegelich ; Betreuer: Simon Hegelich". München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1204562296/34.
Texto completoEk, Adam. "Extracting social networks from fiction : Imaginary and invisible friends: Investigating the social world of imaginary friends". Thesis, Stockholms universitet, Institutionen för lingvistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-145659.
Texto completoJoseph, Kenneth. "New Methods for Large-Scale Analyses of Social Identities and Stereotypes". Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/690.
Texto completoLibros sobre el tema "Computational social networks"
Nguyen, Hien T. y Vaclav Snasel, eds. Computational Social Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42345-6.
Texto completoAbraham, Ajith y Aboul-Ella Hassanien, eds. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1.
Texto completoAbraham, Ajith, ed. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4051-1.
Texto completoAbraham, Ajith, ed. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2.
Texto completoThai, My T., Nam P. Nguyen y Huawei Shen, eds. Computational Social Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21786-4.
Texto completoMohaisen, David y Ruoming Jin, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91434-9.
Texto completoTagarelli, Andrea y Hanghang Tong, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34980-6.
Texto completoChen, Xuemin, Arunabha Sen, Wei Wayne Li y My T. Thai, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04648-4.
Texto completoChellappan, Sriram, Kim-Kwang Raymond Choo y NhatHai Phan, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8.
Texto completoDinh, Thang N. y Minming Li, eds. Computational Data and Social Networks. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26303-3.
Texto completoCapítulos de libros sobre el tema "Computational social networks"
Herbiet, Guillaume-Jean y Pascal Bouvry. "Social Network Analysis Techniques for Social-Oriented Mobile Communication Networks". En Computational Social Networks, 51–80. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_3.
Texto completoReinhardt, Wolfgang, Adrian Wilke, Matthias Moi, Hendrik Drachsler y Peter Sloep. "Mining and Visualizing Research Networks Using the Artefact-Actor-Network Approach". En Computational Social Networks, 233–67. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_10.
Texto completoSalama, Mostafa, Mrutyunjaya Panda, Yomna Elbarawy, Aboul Ella Hassanien y Ajith Abraham. "Computational Social Networks: Security and Privacy". En Computational Social Networks, 3–21. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4051-1_1.
Texto completoHuang, Jiaqing, Qingyuan Liu, Zhibin Lei y Dah Ming Chiu. "Applications of Social Networks in Peer-to-Peer Networks". En Computational Social Networks, 301–27. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_12.
Texto completoPanda, Mrutyunjaya, Nashwa El-Bendary, Mostafa A. Salama, Aboul Ella Hassanien y Ajith Abraham. "Computational Social Networks: Tools, Perspectives, and Challenges". En Computational Social Networks, 3–23. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_1.
Texto completoKundu, Anirban. "Dynamic Web Prediction Using Asynchronous Mouse Activity". En Computational Social Networks, 257–80. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_10.
Texto completoKhodaparast, Ali Asghar y Azade Kavianfar. "PPMN: A City Wide Reliable Public Wireless Mesh Network". En Computational Social Networks, 281–300. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_11.
Texto completoPal, Arpan, Chirabrata Bhaumik, Priyanka Sinha y Avik Ghose. "Intelligent Social Network of Devices". En Computational Social Networks, 329–48. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_13.
Texto completoLuo, Xun. "Social Network-Based Media Sharing in the Ubiquitous Environment: Technologies and Applications". En Computational Social Networks, 349–66. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_14.
Texto completoGeierhos, Michaela y Mohamed Ebrahim. "Customer Interaction Management Goes Social: Getting Business Processes Plugged in Social Networks". En Computational Social Networks, 367–89. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_15.
Texto completoActas de conferencias sobre el tema "Computational social networks"
Dragoni, Mauro. "Computational advertising in social networks". En SAC 2018: Symposium on Applied Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3167132.3167324.
Texto completoSurma, Jerzy, Malgorzata Roszkiewcz y Jacek Wojcik. "Towards Understanding Social Influence in On-Line Social Networks". En 2014 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2014. http://dx.doi.org/10.1109/csci.2014.132.
Texto completoFaghani, Mohammad Reza y Hossein Saidi. "Social Networks' XSS Worms". En 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.424.
Texto completoLajmi, Sonia, Johann Stan, Hakim Hacid, Elöd Egyed-Zsigmond y Pierre Maret. "Extended Social Tags: Identity Tags Meet Social Networks". En 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.106.
Texto completoHamamreh, Rushdi A. y Sameh Awad. "Tag Ranking Multi-agent Semantic Social Networks". En 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.156.
Texto completoZhang, Huiqi, Ram Dantu y Joao Cangussu. "Quantifying Reciprocity in Social Networks". En 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.399.
Texto completoSmith, Marc, Derek L. Hansen y Eric Gleave. "Analyzing Enterprise Social Media Networks". En 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.468.
Texto completo"International Workshop on Computational Social Networks (IWCSN 2011)". En 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.301.
Texto completoZhang, Xiaoqin, Vaishnavi Guduguntla, Kalyani Emani, Gaurav Kulkarni y Pavan Kaparthi. "Get Smart on Information-Sharing in Social Networks". En 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00242.
Texto completoAimeur, Esma, Hicham Hage y Sabrine Amri. "The Scourge of Online Deception in Social Networks". En 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00244.
Texto completoInformes sobre el tema "Computational social networks"
Berry, Nina M., Jessica Glicken Turnley, Julianne D. Smrcka, Teresa H. Ko, Timothy David Moy y Benjamin C. Wu. Computational social network modeling of terrorist recruitment. Office of Scientific and Technical Information (OSTI), octubre de 2004. http://dx.doi.org/10.2172/919633.
Texto completoAfrican Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.
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