Relevant bibliographies by topics / Social networs

Contents

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

Academic literature on the topic 'Social networs'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Social networs"

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Hernández Sanabria, Evelyn. "Influencia de redes sociales en hábitos de estudio de universitarios de primer año." Foro Educacional, no. 22 (July 2, 2014): 131. http://dx.doi.org/10.29344/07180772.22.670.

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Resumen La investigación de la cual se deriva este artículo, se enfocó en el estudio de elementos que permitieron establecer una relación entre el uso de las redes sociales y las repercusiones que tienen en los hábitos de estudio de universitarios de primer año. La investigación fue llevada a cabo en la Universidad de Costa Rica (UCR), sede Central Rodrigo Facio, y se abordó desde un enfoque mixto, lo que implica procesos de recolección, análisis e interpretación de datos de tipo cualitativo y cuantitativo en un mismo estudio. La muestra de participantes fue constituida por un grupo de 50 estudiantes de primer año universitario y tres docentes de la misma población. Como procedimientos para la recolección de información de los participantes, se aplicó una encuesta para los estudiantes y una entrevista a los docentes. Los hallazgos del estudio demostraron el uso continuo de redes sociales por parte de la población estudiantil en cuestión y la influencia que el uso constante de estas provoca en los hábitos de estudio de los jóvenes. Palabras clave: Redes sociales; web 2.0; hábitos de estudio; universitarios; virtualidad; uso de tecnologías. The influence of social networks on the study habits of first-year university students Abstract This research studied the relationship between the use of social networks and its impact on the study habits of first-year university students. The research took place at Universidad de Costa Rica main Rodrigo Facio campus; it included a mixed approach and the collection, analysis and interpretation of both qualitative and quantitative data. The sample consisted of 50 first-year university students and 3 of their professors. A survey was applied to collect information from the students and an interview to get information from the professors. The results show the permanent use of social networks by the students and the influence this use has on the study habits of young people. Key words: Social Networs; Web 2.0; Study Habits; University Students; Virtual World; Use of Technologies. * Artículo derivado de Proyecto final de graduación por el grado de Magíster en Educación con acentuación en Desarrollo Cognitivo del Instituto Tecnológico y de Estudios Superiores de Monterrey, México.
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Jadhav, Pranavati, and Dr Burra Vijaya Babu. "Detection of Community within Social Networks with Diverse Features of Network Analysis." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Fuentes Cancell, Dieter Reynaldo, Odiel Estrada Molina, and Nilda Delgado Yanes. "Las redes sociales digitales: una valoración socioeducativa. Revisión sistemática." Revista Fuentes 1, no. 23 (2021): 41–52. http://dx.doi.org/10.12795/revistafuentes.2021.v23.i1.11947.

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The future and already current industrial revolution 4.0 demand the introduction of a digital transformation in the higher education contributing to the formation of competent professionals, for which, they are evidenced in the pedagogies and emergent technologies, an increase of didactic experiences in the use of the digital social networks. In this research a systematic review to identify current trends in the use of online social networks for educational purposes are performed. PRISMA protocol was used and analyzed 79 studies present in the database Scopus. In the systematic review, the following questions are answered: What types of designs predominate in the scientific literature? What is the dependentindependent pairs of variables? And What are the current trends in the use of digital social networks for educational purposes? As a result of this research, Facebook is reaffirmed as the social network most used by educators and the need for the integrated and varied use of these networks. It concludes with the defense of the following trends: university institutional communication policies from the curricular levels; the development of creativity, cultural convergence and media diversification; educational innovation; media culture and academic digital identity
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Levin, Ilya, Mark Korenblit, and Vadim Talis. "STUDY OF SOCIAL NETWORKS’ DYNAMICS BY SIMULATION WITHIN THE NODEXL-EXCEL ENVIRONMENT." Problems of Education in the 21st Century 54, no. 1 (2013): 125–37. http://dx.doi.org/10.33225/pec/13.54.125.

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The present study is an analysis of the learning activity, which constitutes simulation of networks and studying their functioning and dynamics. The study is based on using network-like learning environments. Such environments allow building computer models of the network graphs. According to the suggested approach, the students construct dynamic computer models of the networks' graphs, thus implementing various algorithms of such networks’ dynamics. The suggested tool for building the models is the software environment comprising network analysis software NodeXL and a standard spreadsheet Excel. The proposed approach enables the students to visualize the network's dynamics. The paper presents specific examples of network models and various algorithms of the network's dynamics, which were developed based on the proposed approach. Key words: learning environments, modelling, social networks.
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Trevillion, S. "Social work, social networks and network knowledge." British Journal of Social Work 30, no. 4 (2000): 505–17. http://dx.doi.org/10.1093/bjsw/30.4.505.

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Sharma, Anita, and Prashant Salwan. "Network Matters! Revisiting Social Networks." Academy of Management Proceedings 2017, no. 1 (2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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Vega Caro, Luisa, and Olga Buzón García. "PRESENCIA SOCIAL DE MUJERES DE ZONAS RURALES EN LAS REDES SOCIALES." Píxel-Bit, Revista de Medios y Educación, no. 48 (2016): 149–63. http://dx.doi.org/10.12795/pixelbit.2016.i48.10.

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Maldonado Brito, Annie Mehes, Brigido Vizeu Camargo, Andréia Isabel Giacomozzi, and Bruna Berri. "Representações sociais do cuidado ao idoso e mapas de rede social." Liberabit: Revista Peruana de Psicología 23, no. 1 (2017): 9–22. http://dx.doi.org/10.24265/liberabit.2017.v23n1.01.

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Kamoru, Balogun Abiodun, Azmi Bin Jaafar, Masrah Azrifah Azmi Murad, and Marzanah A. Jabar. "Understanding Security Threats in Spam Detection on Social Networks." Circulation in Computer Science 2, no. 5 (2017): 18–22. http://dx.doi.org/10.22632/ccs-2017-252-19.

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Social network has become a very popular way for internet users to communicate and interact online. The socia; networks provide a platform to maintain a contact with friends. Increasing social network’s popularity allows all of them to collect large amounts of personal details about their users. Globally, the issue of identifying spammers have received great attention due to its practical relevance in the field of social network analysis. Social network community users are fed with irrelevant information while surfing, due to spammer's activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. The aim of this paper is to examine previous works in the field of spam detection in social networks, the study attempts to review various spam detection frameworks which details about the detection and elimination of spam's in various sources, By classification and Clustering Method of spam detection and by raising security awareness among the users of social networks and stake holders , by prescribing a strategic approach or data mining approach for analyzing the nature of spam detection on social networks.
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Shepherd, Patrick, and Judy Goldsmith. "A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13734–35. http://dx.doi.org/10.1609/aaai.v34i10.7139.

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The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.
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Dissertations / Theses on the topic "Social networs"

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Schuhart, Russell G. "Hacking social networks examining the viability of using computer network attack against social networks." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FSchuhart.pdf.

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Thesis (M.S. in Information Systems and Operations)--Naval Postgraduate School, March 2007.
Thesis Advisor(s): David Tucker. "March 2007." Includes bibliographical references (p. 55-56). Also available in print.
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Araújo, Ricardo Matsumura de. "Memetic networks : problem-solving with social network models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2010. http://hdl.handle.net/10183/25515.

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Sistemas sociais têm se tornado cada vez mais relevantes para a Ciência da Computação em geral e para a Inteligência Artificial em particular. Tal interesse iniciou-se pela necessidade de analisar-se sistemas baseados em agentes onde a interação social destes agentes pode ter um impacto no resultado esperado. Uma tendência mais recente vem da área de Processamento Social de Informações, Computação Social e outros métodos crowdsourced, que são caracterizados por sistemas de computação compostos de pessoas reais, com um forte componente social na interação entre estas. O conjunto de todas interações sociais e os atores envolvidos compõem uma rede social, que pode ter uma forte influência em o quão eficaz ou eficiente o sistema pode ser. Nesta tese, exploramos o papel de estruturas de redes em sistemas sociais que visam a solução de problemas. Enquadramos a solução de problemas como uma busca por soluções válidas em um espaço de estados e propomos um modelo - a Rede Memética - que é capaz de realizar busca utilizando troca de informações (memes) entre atores interagindo em uma rede social. Tal modelo é aplicado a uma variedade de cenários e mostramos como a presença da rede social pode melhorar a capacidade do sistema em encontrar soluções. Adicionalmente, relacionamos propriedades específicas de diversas redes bem conhecidas ao comportamento observado para os algoritmos propostos, resultando em um conjunto de regras gerais que podem melhorar o desempenho de tais sistemas sociais. Por fim, mostramos que os algoritmos propostos são competitivos com técnicas tradicionais de busca heurística em diversos cenários.
Social systems are increasingly relevant to computer science in general and artificial intelligence in particular. Such interest was first sparkled by agent-based systems where the social interaction of such agents can be relevant to the outcome produced. A more recent trend comes from the general area of Social Information Processing, Social Computing and other crowdsourced systems, which are characterized by computing systems composed of people and strong social interactions between them. The set of all social interactions and actors compose a social network, which may have strong influence on how effective the system can be. In this thesis, we explore the role of network structure in social systems aiming at solving problems, focusing on numerical and combinatorial optimization. We frame problem solving as a search for valid solutions in a state space and propose a model - the Memetic Network - that is able to perform search by using the exchange of information, named memes, between actors interacting in a social network. Such model is applied to a variety of scenarios and we show that the presence of a social network greatly improves the system capacity to find good solutions. In addition, we relate specific properties of many well-known networks to the behavior displayed by the proposed algorithms, resulting in a set of general rules that may improve the performance of such social systems. Finally, we show that the proposed algorithms can be competitive with traditional heuristic search algorithms in a number of scenarios.
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Torchi, Ilaria. "MOBILE SOCIAL NETWORK: dalle RETI SOCIAL al SOCIALE in RETE." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15415/.

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I Mobile Social Network consentono alle persone di connettersi fra di loro, condividere interessi comuni, opinioni, informazioni e media. Il fattore alla base che accomuna tutti i tempi è sempre la relazione: gli uomini hanno il bisogno vitale di stare con gli altri e se per farlo, oltre che scambiando foto e video, si trovassero delle vie concrete per crescere in solidarietà ed aumentare il bene comune? Nasce così il desiderio di affrontare lo studio di questa tesi facendo ricerca ed esplorazione attraverso tre tappe ben precise. Il titolo “MOBILE SOCIAL NETWORK: dalle RETI SOCIAL al SOCIALE in RETE” racchiude le tre parti e nell’ordine in cui vengono esposte. Nella prima “Mobile Social Network”, si è affrontata la parte teorica e tecnica che spiega chi sono e come sono fatti i MSN; quindi dalla loro definizione fino all’illustrazione dei componenti, delle architetture e dagli ambiti di applicazione. Nella seconda sezione, “dalle Reti Social” si raccontano i protagonisti social più famosi di oggi attraverso una rassegna sviluppata per punti, ovvero le caratteristiche a fattor comune dei MSN stessi. Nella terza sezione, “al Sociale in Rete”, si sperimenta l’ambito del solidale percorrendo una approfondita ricerca sul web di tutte le possibili applicazioni MSN esistenti che abbiano uno scopo solidale per giungere così alla conclusione della tesi che consiste nella presentazione di un progetto che si propone la creazione di un’app solidale per un’associazione che opera a sostegno di persone in difficoltà: “ARCA MOBILE” una piattaforma per far incontrare domanda ed offerta dove protagonisti sono gli arredi, a sostegno di persone disagiate che non hanno la possibilità di arredare casa o reperire un mobile di cui necessitano.
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You, Bo. "Hub-Network for Distance Computation in Large Social Networks." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1412601464.

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Vallapu, Sai Krishna. "Towards Network False Identity Detection in Online Social Networks." Thesis, Southern Illinois University at Edwardsville, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10246101.

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In this research, we focus on identifying false identities in social networks. We performed a detailed study on different string matching techniques to identify user profiles with real or fake identity. In this thesis, we focus on a specific case study on sex offenders. Sex offenders are not supposed to be online on social networking sites in few states. To identify the existence of offenders in social networks, we ran experiments to compare datasets downloaded from Facebook and offender registries. To identify the most suitable string matching technique to solve this particular problem, we performed experiments on various methods and utilized the most appropriate technique, the Jaro-Winkler algorithm. The major contribution of our research is a weight based scoring function that is capable of identifying user records with full or partial data revealed in social networks. Based on our data samples created using metadata information of Facebook, we were able to identify the sex offender profiles with real identity and seventy percent of the sex offenders with partial information.

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Burks, Stephen D. "Social Networks and Its Uses in Collaborative Strategies." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5094.

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In this paper, there are three policy scenarios that are explored and discussed. The first scenario comes from a dataset where little information is known about individual nodes and connection weights are placed based on the economic theory of increasing or constant returns. The second dataset was derived by taking a group of academic researchers (without any knowledge beyond co authorship alliances) working on a joint venture and exploring what combined research ventures would be most beneficial for future research outputs. More information concerning individual nodes and connections is given in this dataset, but the weights on connections are still developed according to rules of economic theory. The final set of data is developed by viewing the same co-authorship alliances as in the second scenario, but instead the data is examined more thoroughly and more accurate maps of authors connection weights are generated.
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Wood, Samuel Bennett. "Social network coding rate control in information centric delay tolerant networks." Thesis, University of California, Santa Cruz, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1583279.

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Tactical and emergency-response networks require efficient communication without a managed infrastructure in order to meet the requirements of mission critical applications. In these networks, mobility, disruption, limited network resources, and limited host resources are the norm instead of the exception. Despite these constraints, applications must quickly and reliably share data collected from their environment to allow users to coordinate and make critical decisions. Our previous work demonstrates that applying information-centric paradigms to the tactical edge can provide performance benefits over traditional address centric approaches. We expand on this work and investigate how social relationships can be inferred and exploited to improve network performance in volatile networks.

As a result of our investigation, we propose SOCRATIC (SOCial RATe control for Information Centric networks), a novel approach to dissemination that unifies replication and network coding, which takes advantage of social content and context heuristics to improve network performance. SOCRATIC replicates network encoded blocks according to a popularity index metric that captures social relationships, and is shared during neighbor discovery. The number of encoded blocks that is relayed to a node depends on its interest in the data object and its popularity index, i.e., how often and for how long it meets other nodes. We observe that nodes with similar interests tend to be co-located and we exploit this information through use of a generalization of a data object-to-interest matching function that quantifies this similarity. Encoded blocks are subsequently replicated towards the subscriber if a stable path exists. We evaluate an implementation of SOCRATIC through a detailed network emulation of a tactical scenario and demonstrate that it can achieve better performance than the existing socially agnostic approaches.

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Schaefer, Cora. "Personal networks on social network sites (SNS) - Context and personality influences." [S.l. : s.n.], 2008. http://digbib.ubka.uni-karlsruhe.de/volltexte/1000009431.

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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

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ATHANASIOU, THOMAS. "Multi-dimensional analysis of social multi-networks : Analysing a 5-layer social network case study." Thesis, Uppsala universitet, Institutionen för informatik och media, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273908.

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Network theory analysis is applicable to many scientific disciplines (fields) such as biology, statistics and sociology. The social network analysis is one of the various branches of the broader network theory analysis, the social network analysis. It is of high interest among the researchers in social sciences. Social networks have had a significant impact on human civilizations for many centuries. During the last two decades, the main academic interest was addressed towards the research and analysis of a dynamically uprising sector of social networks, the on-line networks, primarily due to the domination of the Internet and technology over human attitudes and relations in modern societies. For many years, research was emphasized on the analysis of simple social networks, whilst during the last decade several researchers started working on the analysis of more complicated social networks, which consist by several smaller social networks. There are important differences between mono and multi-dimensional network analysis. Mono-dimensional analysis provides the research with relevant knowledge. On the other hand, multi-dimensional analysis is still at initial stage. As a result, several potential models related to the multi-networks analysis cannot always provide reliable and adequate outcomes. However, due to the fact that different social networks can be easily combined and form more extended and complicated networks, it is of high importance for the researchers to advance the multi-dimensional analysis and provide more adequate analytical models. The purpose of this thesis is to present the dynamic of the multi-dimensional analysis by consecutively applying both mono and multi-dimensional analysis on a social multi-network. The findings suggest that multi-dimensional analysis can add reliable knowledge on the social network analysis, but many problems that arose due the complexity of the multi-networks structures need to be addressed.
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Books on the topic "Social networs"

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Besitzende Bürger und elende Sieche: Lübecks Gesellschaft im Spiegel ihrer Testamente 1400-1449. Schmidt-Römhild, 2010.

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Alhajj, Reda, H. Ulrich Hoppe, Tobias Hecking, Piotr Bródka, and Przemyslaw Kazienko, eds. Network Intelligence Meets User Centered Social Media Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90312-5.

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Dynamic network theory: How social networks influence goal pursuit. American Psychological Association, 2012.

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Westaby, James D. Dynamic network theory: How social networks influence goal pursuit. American Psychological Association, 2012. http://dx.doi.org/10.1037/13490-000.

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Raatma, Lucia. Social networks. Cherry Lake Pub., 2010.

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author, Belliger Andréa, ed. Interpreting networks: Hermeneutics, actor-network theory & new media. Transcript, 2014.

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The social classroom: Integrating social network use in education. Information Science Referece, 2014.

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Gibson, Shane. Sociable!: How social media is turning sales and marketing upside-down. Stephen Jagger Holdings, 2009.

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Disrupting criminal networks: Network analysis in crime prevention. FirstForumPress, Inc., 2015.

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Treur, Jan. Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31445-3.

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Book chapters on the topic "Social networs"

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Sohn, Jongsoo, Daehyun Kang, Hansaem Park, Bok-Gyu Joo, and In-Jeong Chung. "An Improved Social Network Analysis Method for Social Networks." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7262-5_13.

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Orumwense, Efe F., Thomas J. Afullo, and Viranjay M. Srivastava. "Cognitive Radio Networks: A Social Network Perspective." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27400-3_39.

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Slingerland, Willeke. "Social Capital, Corrupt Networks, and Network Corruption." In Understanding Complex Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81484-7_2.

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Breslin, John G., Alexandre Passant, and Stefan Decker. "Social networks." In The Social Semantic Web. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01172-6_10.

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Gruenbaum, Ronan. "Social Networks." In Making Social Technologies Work. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137024824_9.

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Marques, Oge. "Social Networks." In Innovative Technologies in Everyday Life. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45699-7_4.

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Spanke, Matthias. "Social Networks." In Retail Isn't Dead. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36650-6_9.

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Al-Taie, Mohammed Zuhair, and Seifedine Kadry. "Social Networks." In Advanced Information and Knowledge Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53004-8_4.

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Dongarra, Jack, Piotr Luszczek, Felix Wolf, et al. "Social Networks." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_163.

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Kohli, Martin, and Harald Künemund. "Social Networks." In Ageing, Health and Pensions in Europe. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230307346_6.

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Conference papers on the topic "Social networs"

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Zhang, Daokun, Jie Yin, Xingquan Zhu, and Chengqi Zhang. "User Profile Preserving Social Network Embedding." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/472.

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This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
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Gao, Li, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, and Yue Hu. "Recommendation with Multi-Source Heterogeneous Information." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/469.

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Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.
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Caragiannis, Ioannis, and Evanthia Tsitsoka. "Deanonymizing Social Networks Using Structural Information." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/169.

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We study the following fundamental graph problem that models the important task of deanonymizing social networks. We are given a graph representing an eponymous social network and another graph, representing an anonymous social network, which has been produced by the original one after removing some of its nodes and adding some noise on the links. Our objective is to correctly associate as many nodes of the anonymous network as possible to their corresponding node in the eponymous network. We present two algorithms that attack the problem by exploiting only the structure of the two graphs. The first one exploits bipartite matching computations and is relatively fast. The second one is a local search heuristic which can use the outcome of our first algorithm as an initial solution and further improve it. We have applied our algorithms on inputs that have been produced by well-known random models for the generation of social networks as well as on inputs that use real social networks. Our algorithms can tolerate noise at the level of up to 10%. Interestingly, our results provide further evidence to which graph generation models are most suitable for modeling social networks and distinguish them from unrealistic ones.
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Ren, Fuxin, Zhongbao Zhang, Jiawei Zhang, et al. "BANANA: when Behavior ANAlysis meets social Network Alignment." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/200.

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Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.
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Pereira, Fabiola S. F., Gina M. B. Oliveira, and João Gama. "User Preference Dynamics on Evolving Social Networks - Learning, Modeling and Prediction." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8129.

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The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks. Online social networks contain rich information about social interactions and relations, becoming essential source of knowledge for the understanding of user preferences evolution. In this thesis, we investigate the interplay between user preferences and social networks over time. We use temporal networks to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Our findings show that we can predict user preference changes by just observing how her social network structure evolves over time.
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Wang, Mingxian, Wei Chen, Yun Huang, Noshir S. Contractor, and Yan Fu. "A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46764.

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Analytical modeling of customer preferences in product design is inherently difficult as it faces challenges in modeling heterogeneous human behavior and product offerings. In this paper, the customer-product interactions are viewed as a complex socio-technical system and analyzed using social network theory and techniques. We propose a Multidimensional Customer-Product Network (MCPN) framework, where separate networks of “customers” and “products” are simultaneously modeled, and multiple types of relations, such as consideration and purchase, product associations, and customer social networks are considered. We start with the simplest unimodal network configuration where customer cross-shopping behaviors and product similarities are analyzed to inform designers about the implied product competition, market segmentation, and product positions in the market. We then progressively extend the network to a multidimensional structure that integrates customer preference decisions with product feature similarities to enable the modeling of preference heterogeneity, product association and decision dependency. Finally, social influences on new product adoption are analyzed in the same framework by introducing customer-customer relations together with other product-product and customer-product relations. Beyond the traditional network descriptive analysis, we employ the Exponential Random Graph Model (ERGM) as a unified statistical inference framework for analyzing multiple relations in MCPN to support engineering design decisions. Our approach broadens the traditional utility-based logit approaches by considering the dependency among product choices and the “irrationality” of customer behavior induced by social influence. While this paper is focused on presenting the conceptual framework of the proposed methodology, examples on customer vehicle preferences are presented to illustrate the progressive development of the MCPN framework from a simple unimodal configuration to a complex multidimensional structure.
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Llorent-Vaquero, Mercedes, África M. Cámara-Estrella, Elena M. Díaz-Pareja, and Juana M. Ortega-Tudela. "USE OF SOCIAL NETWORKS FOR THE TRAINING OF FUTURE TEACHERS IN EARLY CHILDHOOD EDUCATION." In International Conference on Education and New Developments. inScience Press, 2021. http://dx.doi.org/10.36315/2021end068.

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Currently, social networks have a great potential in the educational field, being useful to promote motivation, the active role of students, communication or flexibility in time and space, among others. In this line, this paper shows an experience of educational innovation in higher education mediated by the use of social networks. Specifically, the social network Instagram was used with students in the second year of the Early Childhood Education Degree at the University of Jaen. The objective of this study was to analyze the impact of the use of social networks on aspects related to the quality of the educational process, such as motivation, creativity, communication or monitoring. In order to respond to the proposed objective, a quantitative methodology was used, with a descriptive method and a survey design. The data collection instrument was a student questionnaire through which the impact of the use of the social network Instagram in the teaching-learning process was evaluated. The results point to a positive effect of the use of social networks on the variables under study. It should be noted how the use of this social network has influenced the motivation towards their learning and the communication processes that were developed with all those involved.
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Ivanova, Galina. "SOCIAL NETWORKS OPPORTUNITIES FOR SUSTAINABLE AGRIBUSINESS DEVELOPMENT." In AGRIBUSINESS AND RURAL AREAS - ECONOMY, INNOVATION AND GROWTH 2021. University publishing house "Science and Economics", University of Economics - Varna, 2021. http://dx.doi.org/10.36997/ara2021.112.

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Social networks are increasingly contributing to sustainable development of agribusiness organizations in today`s conditions. Building networks that work through interactions between business organizations is influenced by current realities, where the business environment is rapidly changing. Relationships between agribusiness organizations in social network could impact of economic development on each individual organization. They lead to improve development of the entire network and higher efficiency.
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Maknickienė, Nijolė, and Lina Rapkevičiūtė. "A STUDY ON SOCIAL MEDIA OPINION ABOUT WOMEN INVESTORS." In International Scientific Conference „Contemporary Issues in Business, Management and Economics Engineering". Vilnius Gediminas Technical University, 2021. http://dx.doi.org/10.3846/cibmee.2021.625.

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Purpose – to investigate opinions on social networks about women’s investment and its determinants. Social network sentiment research aims to find out why investing remains a very masculine area of life. Research methodology – Twitter social network analysis tools will be used for data mining. Word clouds and sentiment index will be obtained using neural network classification algorithm based on Long Short-Term Memory (LSTM). Findings – the paper obtained the dynamics of three-week opinions on the social network Twitter, considering the main factors that influence women’s choice to invest. Research limitations – only the main factors were investigated and only based on a survey of other authors. Data were extracted from the social network for a limited time. Practical implications – traditionally, investing has remained an area dominated by men. However, women are be-coming increasingly financially independent and increasingly involved in the investment process. Therefore, it is very important to analyze the factors that hinder the achievement of investment results. Originality/Value – there are many scientific papers that examine the factors that determine women’s investment choices. However, opinions and sentiments on social networks have not been explored.
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Barbosa, Gabriel, Thaiana Lima, Rodrigo Santos, and Claudia Werner. "Brechó-SocialSECO: Uma Ferramenta para Apoiar Modelagem e Análise de Ecossistemas de Software." In IV Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/brasnam.2015.6793.

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Em todo sistema de software, existe interação entre componentes. Ao unir uma rede técnica de artefatos com um ambiente social, cria-se uma rede socio-técnica. Visando desenvolver uma ferramenta que modele, analise e incentive as relações sociais na rede socio-técnica, foi proposta a Brechó SocialSECO. Estendeu-se a biblioteca de componente Brechó para melhor entender ecossistemas de software e dar suporte para tomadas de decisão. Este trabalho apresenta a ferramenta e seu funcionamento.
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Reports on the topic "Social networs"

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Kilduff, Martin J., Blaine Landis, and Jochen Menges. Emotion and Social Network Perceptions: How Does Anger Bias Perceptions of Networks? Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580179.

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Malde, Bansi, and Arun Advani. Empirical methods for networks data: social effects, network formation and measurement error. IFS, 2014. http://dx.doi.org/10.1920/wp.ifs.2014.1434.

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McCulloh, Ian A., and Kathleen M. Carley. Social Network Change Detection. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487504.

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Vyncke, E. IPv6 over Social Networks. RFC Editor, 2009. http://dx.doi.org/10.17487/rfc5514.

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Cullen, Kristen, Charles Palus, and Craig Appaneal. Developing network perspective: Understanding the basics of social networks and their role in leadership. Center for Creative Leadership, 2014. http://dx.doi.org/10.35613/ccl.2014.1019.

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McCulloh, Ian, Matthew Webb, John Graham, Kathleen Carley, and Daniel B. Horn. Change Detection in Social Networks. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada484175.

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Bailey, Michael, Ruiqing Cao, Theresa Kuchler, and Johannes Stroebel. Social Networks and Housing Markets. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22258.

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Fogli, Alessandra, and Laura Veldkamp. Germs, Social Networks and Growth. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18470.

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Figlio, David, Sarah Hamersma, and Jeffrey Roth. Information Shocks and Social Networks. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w16930.

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Macskassy, Sofus. Evolve: Analyzing Evolving Social Networks. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada564174.

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