Relevant bibliographies by topics / Social network

Academic literature on the topic 'Social network'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Social network"

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Trevillion, S. "Social work, social networks and network knowledge." British Journal of Social Work 30, no. 4 (August 1, 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 (August 2017): 17317. http://dx.doi.org/10.5465/ambpp.2017.17317abstract.

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Gilles, Robert, Tabitha James, Reza Barkhi, and Dimitrios Diamantaras. "Simulating Social Network Formation." International Journal of Virtual Communities and Social Networking 1, no. 4 (October 2009): 1–20. http://dx.doi.org/10.4018/jvcsn.2009092201.

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Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.
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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 (December 31, 2019): 366–71. http://dx.doi.org/10.5373/jardcs/v11sp12/20193232.

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Treese, Win. "Social network?" netWorker 12, no. 1 (March 2008): 13–15. http://dx.doi.org/10.1145/1346942.1346946.

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Bongomin, George Okello Candiya, John C. Munene, Joseph Mpeera Ntayi, and Charles Akol Malinga. "Social network." African Journal of Economic and Management Studies 9, no. 3 (September 3, 2018): 388–406. http://dx.doi.org/10.1108/ajems-07-2017-0157.

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Purpose The purpose of this paper is to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Design/methodology/approach The study employed a cross-sectional research design and data were collected from a total of 400 poor households located in Northern, Eastern, Central and Western Uganda. The authors adopted ordinary least square hierarchical regression analysis to test for the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. The effects were determined by calculating the significant change in coefficient of determination (R2) between the dimensions of social network in explaining financial inclusion. In addition, analysis of variance was also used to test for variation in perceptions of the poor about being financially included. Findings The findings revealed that the dimensions of ties and interaction significantly explain financial inclusion of the poor in rural Uganda. Contrary to previous studies, the results indicated that interdependence as a dimension of social network is not a significant predictor of financial inclusion of the poor in rural Uganda. Combined together, the dimensions of social network explains about 16.6 percent of the variation in financial inclusion of the poor in rural Uganda. Research limitations/implications The study was purely cross-sectional, thus, ignoring longitudinal survey design, which could have investigated certain characteristics of the variable over time. Additionally, although a total sample amounting to 400 poor households was used in the study, the results cannot be generalized since other equally marginalized groups such as the disabled persons, refugees, and immigrants were not included in this study. Furthermore, the study used only the questionnaire to elicit responses from the respondents. The use of interview was ignored during data collection. Practical implications Policy makers, managers of financial institutions, and financial inclusion advocates should consider social network dimensions of ties and interaction as conduits for information flow and sharing among the poor including the women and youth about scarce financial resources like loans. Advocacy towards creation of societal network that brings the poor together in strong and weak ties is very important in scaling up access to and use of scarce financial services for improving economic and social well-being. Originality/value Contrary to previous studies, this particular study test the predictive power of each of the dimensions of social network in explaining financial inclusion of the poor in rural Uganda. Thus, it methodologically isolates the individual contribution of each of the dimensions of social network in explaining financial inclusion of the poor. The authors found that only ties and interaction are significant predictors of financial inclusion of the poor in rural Uganda. Therefore, the findings suggest that not all dimensions of social network are significant predictors of financial inclusion as opposed to previous empirical findings.
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Bolster, Mary. "Social Network." Brain & Life 16, no. 5 (October 2020): 38. http://dx.doi.org/10.1097/01.nnn.0000719040.69363.cd.

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Williams, Ruth, and Andrea Shaw. "Social network." Nursing Management 22, no. 6 (September 30, 2015): 17. http://dx.doi.org/10.7748/nm.22.6.17.s24.

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Kutz, Giorgio. "Social network." Giornale della L 12, no. 6 (June 20, 2013): 47–50. http://dx.doi.org/10.1472/ser-art-ve.

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Usui, Shohei, Fujio Toriumi, Masato Matsuo, Takatsugu Hirayama, and Kenji Mase. "Greedy Network Growth Model of Social Network Service." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (July 20, 2014): 590–97. http://dx.doi.org/10.20965/jaciii.2014.p0590.

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As new network communication tools are developed, social network services (SNS) such as Facebook and Twitter are becoming part of a social phenomenon globally impacting on society. Many researchers are therefore studying the structure of relationship networks among users. We propose a greedy network growth model that appropriately increases nodes and links while automatically reproducing the target network. We handle a wide range of networks with high expressive ability. Results of experiments showed that we accurately reproduced 92.4% of 189 target networks from real services. The model also enabled us to reproduce 30 networks built up by existing network models. We thus show that the proposed model represents the expressiveness of many existing network models.
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Dissertations / Theses on the topic "Social network"

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Kyrychok, B. V. "Social network." Thesis, Сумський державний університет, 2013. http://essuir.sumdu.edu.ua/handle/123456789/33691.

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Social networking online involves using Websites to share information with others and connect with them by creating a profile that may include a personal Webpage and a blog. Social networking sites allow users to add friends, send messages and comment on others' profile pages. Social networking has many negatives and positives impact. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/33691
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Mannapperuma, Chanaka. "Tangible Social Network System : Visual Markers for Social Network." Thesis, Umeå University, Department of Informatics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34927.

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Tangible social network system is a home-based communication solution specifically designed for elders. Former researches indicate that insufficient communication among elders cause several challenges in their daily activities such as social isolation, loneliness, depression and decreased appetite. In addition, lack of social participation increases the risk of Alzheimer´s (Ligt Enid, 1990). The major cause of these challenges are that elders are increasingly removed from communication technology using emails, text messaging, interact with social network systems and mobile phones due to cognitive and physical difficulties. To overcome this problem, new suggested social network system incorporates photo frame and photo album based interaction which allows instantaneous participation to the social network. By designing the new social network system, I tried to create an easier venue for more active cross-generational communication between elders and younger family members.This paper discusses the early results of the marker based social networking system aiming to propose digital technologies to enhance the social life of older people, who live alone their home. A prototype combining a touch screen, photo frame and a camera are described. It allows the older people to manage their participation to the social network system and get in touch with their loved ones. This paper demonstrates a User Sensitive inclusive Design (USID) process from the generation of user needs to the evaluation prototype. A key theme of tangible social network system shows how usable and emotional design derived from a user inclusive design process can encourage elders to adopt new modern technology. A first evaluation has shown the usability as well as the good acceptance of this system.


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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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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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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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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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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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Sun, Lei. "Mobile Social Network Platform." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-344871.

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The SWiN project is an abbreviation for Social Wireless Network Secure Identification project and it primarily focuses on the security issues of social networks on mobile platforms. This master thesis is a part of the SWiN project from SICS(Swedish Institute of Computer Science) in cooperation with Ericsson and Sony. In this thesis project, we have designed and implemented a social networkingprototype called Friend Finder. This prototype integrates different security solutions such as SAML and GBA to test the performance of them.
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Shaikh, Sajid S. "Computations in social network." [Kent, Ohio] : Kent State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1185560088.

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Vetro, Carla. "La social network analysis nella valutazione delle politiche sociali." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/341.

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2009 - 2010
Il tema della valutazione emerge periodicamente nella discussione politica italiana. L’azione del valutare, che rappresenta ormai un’operazione ricorrente nella vita quotidiana, diviene una pratica consolidata anche in seno alle istituzioni pubbliche, indispensabile per costruire un giudizio sul funzionamento delle politiche stesse. La pratica valutativa si rivela, però, difficile da applicare in contesti complessi e dinamici come quelli che caratterizzano gli interventi nel sociale, dove la complessità attiene alla eterogeneità e pluralità di attori coinvolti e alla multiproblematicità dei bisogni territoriali. Quando la riuscita di una politica di intervento dipende non solo dalle capacità di coordinamento dall’alto, cioè di chi programma gli interventi sociali e offre i servizi per rispondere ai bisogni di una comunità, ma anche dalla volontà e dalla partecipazione dal basso, cioè di chi fruisce degli interventi, risulta chiaro quanto un processo di valutazione diventi complesso. In tali situazioni, le tecniche della Social Network Analysis (di seguito analisi delle reti sociali) risultano particolarmente adatte a rilevare, studiare ed interpretare le interazioni di tutti gli attori coinvolti in uno o più interventi di politica sociale. Tali tecniche di analisi vengono utilizzate sempre più spesso nella ricerca valutativa, in quanto si presuppone che ci possa essere una relazione fra le caratteristiche della rete, costituita dagli attori sociali coinvolti nell’attuazione di un programma, e l’efficacia del programma stesso. [a cura dell'autore]
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Books on the topic "Social network"

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Freeman, Linton. Social Network Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2008. http://dx.doi.org/10.4135/9781446263464.

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Knoke, David, and Song Yang. Social Network Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2008. http://dx.doi.org/10.4135/9781412985864.

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Knoke, David. Social network analysis. 2nd ed. Los Angeles: Sage Publications, 2008.

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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C, Freeman Linton, ed. Social network analysis. Los Angeles: SAGE, 2008.

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I social network. Bologna: Il mulino, 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. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90312-5.

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Abraham, Ajith, Aboul-Ella Hassanien, and Vaclav Sná¿el, eds. Computational Social Network Analysis. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-229-0.

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Aggarwal, Charu C., ed. Social Network Data Analytics. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8462-3.

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service), SpringerLink (Online, ed. Social Network Data Analytics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Book chapters on the topic "Social network"

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Klärner, Andreas, and Holger von der Lippe. "Social Network Mechanisms." In Social Networks and Health Inequalities, 49–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97722-1_4.

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AbstractThe influence and significance of social networks in health research are becoming widely discussed. Sociological network research meets the demand for a stronger consideration of “contexts” or the “environment” that influences health and care. Social networks are conceived as a mediating meso-level, which mediates between social macro-structures (e.g., healthcare systems, institutions, and organizations) and individual (not always) rationally acting actors. This perspective offers the possibility to analyze a variety of psychosocial mechanisms. These mechanisms can influence individual health in different ways, including (health) behavior, psyche, or physiology. In this chapter we present some central theoretical concepts, as well as empirical results, on network effects under the headings of “social support,” “social integration,” “social influence,” and “social contagion.”
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Aggrawal, Niyati, and Adarsh Anand. "Network Centrality." In Social Networks, 53–66. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-4.

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Aggrawal, Niyati, and Adarsh Anand. "Network Cohesion." In Social Networks, 155–72. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-9.

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Aggrawal, Niyati, and Adarsh Anand. "Network Models." In Social Networks, 37–52. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003088066-3.

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Whitehead, James, and Mike Peckham. "Social Network Analysis." In Network Leadership, 105–19. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003092582-17.

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Kale, Vivek. "Social Networks." In Agile Network Businesses, 403–15. New York : CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.4324/9781315368559-21.

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Pignataro, Carlos, and Joe Marcus Clarke. "Network-Embedded Social Network Protocols." In Network-Embedded Management and Applications, 195–216. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-6769-5_10.

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Chambers, Deborah. "Network Society." In New Social Ties, 113–32. London: Palgrave Macmillan UK, 2006. http://dx.doi.org/10.1057/9780230627284_7.

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Bastos, Marco. "Network spillover." In Spatializing Social Media, 53–62. London: Routledge, 2021. http://dx.doi.org/10.4324/9780429354328-8.

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Whitehead, James, and Mike Peckham. "Actor and Social Networks." In Network Leadership, 75–89. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003092582-14.

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Conference papers on the topic "Social network"

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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. California: 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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Silvestru, Catalin ionut, Marianernut Lupescu, Virgil Ion, and Ramona camelia Silvestru. "CONCEPT: SOCIAL NETWORK EDUCATIONAL PLATFORM." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-077.

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Due to the growing involvement of social networks in economic, politic and social aspects of people’s lives and taking into account that social networks represent the main ICT means of socializing, having a steady increase of users each year, we the authors consider that social networks can have a positive impact on education, and also, if combined with educational tools, can be used efficiently in a non-formal and informal educational purpose. Thus, this paper sets out to analyse the current use of social networks in educational purposes in order to understand the impact social networks have on education, either positive or negative, and afterwards the authors will create a theoretical model of how a social network for educational purposes should work after analysing the current theoretical functionalities that such a social tool has and comparing them with the theoretical functionalities that an educational platform has. Thus, by analysing the main aspects in regards to how social networks function and how people interact with them, and also doing the same for educational platforms, we the authors will construct a theoretical model that combines the aspects that we consider are necessary for a Social Network Educational Platform to have in order to balance out each negative aspect that both sides have in regards with what user feedback has offered over the last years. The statistical analysis, that will be presented in order to show the importance of social networks in non-formal and informal education and also information on the use of e-learning platforms, will be focused on absolute and relative statistical indicators. The authors believe that the concept used in this current paper can be used as a guideline for further developing more social network educational platforms and tools that take into account Social Media, Social Networks and have a more positive impact on how adults and students see and use ICT for informal and non-formal learning purposes.
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Perez, Luis G., Beatriz Montes-Berges, and Maria del Rosario Castillo-Mayen. "Boosting social networks in Social Network-Based Recommender System." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121693.

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Zhao, Guofeng, Dan Li, Chuan Xu, Hong Tang, and Shui Yu. "Network dynamics of mobile social networks." In ICC 2014 - 2014 IEEE International Conference on Communications. IEEE, 2014. http://dx.doi.org/10.1109/icc.2014.6883695.

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Zhang, Nan (Andy), and Chong (Alex) Wang. "Network Overlap and Network Blurring in Online Social Networks." In 18th International Conference on Enterprise Information Systems. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005911203270332.

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Ren, Fuxin, Zhongbao Zhang, Jiawei Zhang, Sen Su, Li Sun, Guozhen Zhu, and Congying Guo. "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}. California: 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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Myers, Seth A., Aneesh Sharma, Pankaj Gupta, and Jimmy Lin. "Information network or social network?" In the 23rd International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2576939.

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Tapiador, Antonio, Diego Carrera, and Joaquin Salvachua. "Social Stream, a social network framework." In 2012 International Conference on Future Generation Communication Technology (FGCT). IEEE, 2012. http://dx.doi.org/10.1109/fgct.2012.6476557.

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Chen Xing. "The Social Network." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6010876.

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Ji, Seung-Hyun, Woo-Keun Chung, and Hwan-Gue Cho. "Extended social network." In the 8th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1670252.1670263.

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Reports on the topic "Social network"

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

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Casasbuenas, Guillermo. Colombia: Solidarity Social Network. Inter-American Development Bank, December 2001. http://dx.doi.org/10.18235/0012249.

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Over the last decades, social policy has been conceived from a sectorial perspective. This means that social policy responds more to government's organizational activities than to people's necessities; as if social problems could be approached from a unilateral perspective; in the same way that national, provincial or municipal budgets are allocated by "sectors".
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Bonnett, Michaela, Chimdi Ezeigwe, Meaghan Kennedy, and Teri Garstka. Using Social Network Analysis to Link Community Health and Network Strength. Orange Sparkle Ball, July 2023. http://dx.doi.org/10.61152/scsf6662.

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Social network analysis (SNA) is a technique used to analyze social networks, whether it be composed of people, organizations, physical locations, or objects. It is being increasingly applied across a variety of sectors to gain insight into patterns of behavior and connectivity, the flow of information and behaviors, and to track and predict the effectiveness of interventions or programs. A key area associated with network strength using SNA is the health and wellness of individuals and communities. Both network strength and health and wellness are measured in many ways, which can obfuscate the association, so more consistency and further research is required. Despite this, the existing research using SNA to link characteristics of social networks to health and wellness find that stronger, more connected networks tend to be associated with better health outcomes. These results also present opportunities and insights for effective program implementation in response to disasters, to increase resilience, and to improve outcomes for individuals and communities.
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Nentwich, Michael, ed. Potential and Impacts of Cloud Computing Services and Social Network Websites: Annex - Social Network Websites. Vienna: self, 2014. http://dx.doi.org/10.1553/ita-pb-a65-8.

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Johnson, Eric M., and Robert Chew. Social Network Analysis Methods for International Development. RTI Press, May 2021. http://dx.doi.org/10.3768/rtipress.2021.rb.0026.2105.

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Social Network Analysis (SNA) is a promising yet underutilized tool in the international development field. SNA entails collecting and analyzing data to characterize and visualize social networks, where nodes represent network members and edges connecting nodes represent relationships or exchanges among them. SNA can help both researchers and practitioners understand the social, political, and economic relational dynamics at the heart of international development programming. It can inform program design, monitoring, and evaluation to answer questions related to where people get information; with whom goods and services are exchanged; who people value, trust, or respect; who has power and influence and who is excluded; and how these dynamics change over time. This brief advances the case for use of SNA in international development, outlines general approaches, and discusses two recently conducted case studies that illustrate its potential. It concludes with recommendations for how to increase SNA use in international development.
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McCulloh, Ian, Kathleen Carley, and Matthew Webb. Social Network Monitoring of Al-Qaeda. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada488339.

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Schmidt, Teresa. Statistical Analysis of Social Network Change. Portland State University Library, December 2019. http://dx.doi.org/10.15760/etd.7288.

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Herrero-Gutiérrez, Francisco-Javier, Alejandro Álvarez-Nobell, and Maricela López-Ornelas. Revista Latina de Comunicación Social, in the social network Facebook. Revista Latina de Comunicación Social, 2011. http://dx.doi.org/10.4185/rlcs-66-2011-944-526-548-en.

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Röders, Jonathan, and Erin McFee. Research Brief: Disengagement as a Social Network Enterprise. Trust After Betrayal, November 2022. http://dx.doi.org/10.59498/46887.

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This research brief describes the disengagement process of formerly armed actors (FAAs) from a Non-State Armed Group (NSAG) as a social network enterprise. It characterises leaving an NSAG as a relational process shaped by various nodes in a defector's network and actors at multiple societal levels such as family and community members, other FAAs, government officials, and (I)NGO professionals, varying across conflict settings and individual cases. The analysis emphasises the importance of relationships in informing individual disengagement decisions and the logistical support required for successful outcomes. It underscores the need to understand how social networks function and recommends to the institutions involved in Disarmament, Demobilisation and Reintegration (DDR) meaningful engagement with the vast range of actors implicated in this process.
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Bowman, Elizabeth K., Nkonko Kamwangamalu, Heather Roy, Alla Tovares, Sue Kase, Michelle Vanni, Mugizi R. Rwebangira, and Mohamed Chouikha. Exploring Social Meaning in Online Bilingual Text through Social Network Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada622463.

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