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Artykuły w czasopismach na temat „Social networl analysis”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 5 lipca 2025

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1

Reis, Eduardo, Magno José Alves, Camilo Dias Seabra, et al. "Socio-environmental assessment of the Santos and São Vicente municipalities on the coast of São Paulo/Brazil: application of social network analysis to the DPSIR conceptual framework." Revista Processando o Saber 17, no. 01 (2025): 261–78. https://doi.org/10.5281/zenodo.15499049.

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Considering the social, economic, and ecological complexity of coastal regions and the difficulty of public and private environmental management in these highly populated areas with multiple sources of pollution, this study used the driver, pressure, state, impact, and response model to socio-environmentally assess the coastal municipalities of Santos and São Vicente in the Baixada Santista metropolitan region, SP-Brazil. Data were evaluated by social network analysis. Results indicated the most relevant drivers (i.e., those that most affected sustainability and environmental governance) within the municipalities of Santos and São Vicente, such as port activities (represented by the pressure due to the emission of contaminants in the air and water and dredging). Poor air quality due to pollution and its impacts on human health stand out as an element in the state factor. The most forceful responses refer to the increase in the inspection and control of atmospheric emissions, liquid effluents, dredging, and solid waste. Our approach will support decision-makers to focus on the most critical aspects in this study to implement corrective and preventive actions and develop environmental and sectoral public policies.

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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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Shcherbakov, V. S., and I. A. Karpov. "Regional Inflation Analysis Using Social Network Data." Economy of Regions 20, no. 3 (2024): 930–46. http://dx.doi.org/10.17059/ekon.reg.2024-3-21.

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Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by a range of factors, including inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of the Internet, especially social networks. It is hypothesised that people search, read, and discuss mainly only those issues that are of particular interest to them. It is logical to assume that the dynamics of prices may also be in the focus of users’ discussions. So, such discussions could be regarded as an alternative source of more rapid information about inflation expectations. This study is based on unstructured data from VKontakte social network used to analyse upward and downward inflationary trends (on the example of the Omsk region). The sample of more than 8.5 million posts was collected between January 2010 and May 2022. The authors used BERT neural networks to solve the problem. These models demonstrated better results than the benchmarks (e.g., logistic regression, decision tree classifier, etc.). It makes possible to define pro-inflationary and disinflationary types of keywords in different contexts and get their visualisation with SHAP method. This analysis provides additional operational information about inflationary processes at the regional level The proposed approach can be scaled for other regions. At the same time, the limitation of the work is the time and power costs for the initial training of similar models for all regions of Russia.

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Almahmoud, Essam, and Hemanta Kumar Doloi. "Assessment of Social Sustainability in Construction Projects Using Social Network Analysis." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 6 (2018): 35–46. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.36.3003.

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This paper aims to propose a framework that puts the stakeholders at the forefront of achieving sustainability in the social context. This research, thus, argues that the social sustainability outcomes in construction are best achieved by taking into account the satisfaction of the stakeholders. Based on sustainability and equity theories, a dynamic assessment model has been developed to evaluate the contributions of projects in a social context. Multiple stakeholders and their differing interests associated with the construction projects have been integrated using social network analysis. The mapping of the relationships between the project stakeholders, with respect to their relative stakes and seven social core functions, have been integrated into the assessment model. The findings of this research suggest that the degree of satisfying the needs of diverse stakeholders is highly significant in achieving social sustainability performance of projects. Using a case study from Saudi Arabia, the applicability and significance of the assessment model has been demonstrated. The application of the model provides the opportunity to identify any problems and to enhance the overall performance of projects in the social context. The functionality and efficacy of the model need to be further tested outside the Saudi Arabian region. The research is original in the sense that for the first time, a novel approach has been developed, putting the stakeholders at the forefront of achieving sustainability outcomes in construction projects

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S., Geetha. "Big Data Analysis - Cybercrime Detection in Social Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 147–52. http://dx.doi.org/10.5373/jardcs/v12sp4/20201476.

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Ashlesha, S. Nagdive, Tugnayat Rajkishor, and Peshkar Atharva. "Social Network Analysis of Terrorist Networks." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2553–59. https://doi.org/10.35940/ijeat.C5431.029320.

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Terrorist Activities worldwide has led to the development of sophisticated methodologies for analyzing terrorist groups and networks. Ongoing and past research has found that Social Network Analysis (SNA) is most effective method for predictive counter-terrorism. Social Network Analysis (SNA) is an approach towards analyzing the terrorist networks to better understand the underlying structure of a network and to detect key players within the network and their links throughout the network. It is also need of the hour to convert available raw data into valuable information for the purpose of global security. Comparative study among SNA tools testify their applicability and usefulness for data gathered through online and offline social sources. However it is advised to incorporate temporal analysis using data mining methods, to improve the capability of SNA tools to handle dynamic social media data. This paper examine various aspects of Social Network Analysis as applied to terrorism, taking empirical data, and open source data based studies into account. This work primarily focuses on different types of decentralized terrorist networks and nodes. The nodes can be classified as organizations, places or persons. We take help of varied centrality measures to identify key players in this network.

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Del Fresno García, Miguel. "Connecting the Disconnected: Social Work and Social Network Analysis. A Methodological Approach to Identifying Network Peer Leaders." Arbor 191, no. 771 (2015): a209. http://dx.doi.org/10.3989/arbor.2015.771n1011.

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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.

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Davel, Ronel, Adeline S. A. Du Toit, and Martie M. Mearns. "Understanding Knowledge Networks Through Social Network Analysis." International Journal of Knowledge Management 13, no. 2 (2017): 1–17. http://dx.doi.org/10.4018/ijkm.2017040101.

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Social network analysis (SNA) is being increasingly deployed as an instrument to plot knowledge and expertise as well as to confirm the character of connections in informal networks within organisations. This study investigated how the integration of networking into KM can produce significant advantages for organisations. The aim of the research was to examine how the interactions between SNA, Communities of Practice (CoPs) and knowledge maps could potentially influence knowledge networks. The researchers endeavour to illustrate via this question that cultivating synergies between SNA, CoPs and knowledge maps will enable organisations to produce stronger knowledge networks and ultimately increase their social capital. This article intends to present a process map that can be useful when an organisation wants to positively increase its social capital by examining influencing interactions between SNA, CoPs and knowledge maps, thereby enhancing the manner in which they share and create knowledge.

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AVCI, UMMUHAN, and ESIN ERGUN. "Öğrencilerin Kişilik Özellikleri ve Performanslarına İlişkin Bir SosyalAğ Analizi." KIRŞEHİR EĞİTİM FAKÜLTESİ DERGİSİ 18, no. 3 (2017): 847–64. http://dx.doi.org/10.29299/kefad.2017.18.3.044.

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Gadiparthi, Manjunath, and E. Srinivasa Reddy. "Impact of Individuals’ Engagement in Social Network-An Extensive Analysis." Webology 19, no. 1 (2022): 2782–96. http://dx.doi.org/10.14704/web/v19i1/web19185.

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Social Network (SN) is of avail for sharing information among individuals and communities for different purposes like sharing opinions, feelings, photos, videos and many others. Since the start of the COVID-19 epidemic and the ensuing limitations, the use of Apps on smart devices has exploded. In-line with how much time is spent on SN by a person, the manifestation of physical and mental problems are found in diverse patterns. In this review a comparative account is presented linking the time spent by individuals on social network and the patterns of the resultant health problems in course of time. Most of the earlier studies categorize the users in to various groups based on the time spent on social network. Then they describe the apparent problems that are faced, under two categories, due to the extensive time spent by the users on various social network applications. Finally, the review presents a comprehensive idea of the different analytical techniques used for finding problems faced with respect to the time spent and frequency of social network use. The results on the whole present a variegated picture as regards existence of correlation between intensity of usage and incidence of health problems.

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UGURLU, Zeynep. "Social Network Analysis of the Farabi Exchange Program: Student Mobility." Eurasian Journal of Educational Research 16, no. 65 (2016): 1–35. http://dx.doi.org/10.14689/ejer.2016.65.18.

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Garcia-Garcia, Fran J., Inmaculada López-Francés, and Cristian Molla-Esparza. "Análisis de redes sociales para la inclusión entre iguales en discusiones en línea con estudiantes de universidad." Pixel-Bit, Revista de Medios y Educación, no. 66 (2023): 7–29. http://dx.doi.org/10.12795/pixelbit.95555.

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Las discusiones asíncronas en línea (DAL) sirven para aprender en la universidad, pero pierden eficacia conforme disminuye la participación de los estudiantes. Este estudio aporta un método basado en el Análisis de Redes Sociales (ARS) que aborda este problema, identificando estudiantes con un alto potencial de inclusión entre iguales durante los debates asíncronos. Para probar el método, configuramos foros de discusión en Moodle y examinamos las interacciones de 93 estudiantes de grado en el área de Ciencias de la Educación. Analizamos las redes sociales que surgieron de los debates, incluyendo 1818 conexiones. Los resultados mostraron que algunos estudiantes tenían más centralidad de cercanía y eran más accesibles que el resto. Una vez identificados, el profesorado pudo animar a estos estudiantes a incluir a quienes participaban menos en los debates. La inclusión de los compañeros tenía sentido cuando participaban en el debate sin obtener una respuesta de sus comentarios fácilmente. Este estudio abre la puerta a más investigación sobre la eficacia de las estrategias docentes basadas en el ARS, concretamente sobre la eliminación de las barreras al aprendizaje y la participación en una DAL con estudiantes universitarios

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M., Shankar. "Social Network Analysis." Shanlax International Journal of Commerce 7, S1 (2019): 259–60. https://doi.org/10.5281/zenodo.3451720.

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Social Network analysis is an investigation made on social structures through the use of networks. The field of social network analysis is the dynamic and highly adaptable combination of techniques that let us quantify and recognize the complex structures and flows of relationships, thoughts, and things between people around the world.     Social network analysis has emerged as a critical technique in modern sociology. It has also gained an important following in economics, geography, history, information science, organizational studies, anthropology, biology, demography, communication studies, sociolinguistics, political science, social psychology, development studies, and computer science and is now usually available as a consumer tool.

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Quinn, Darren, Liming Chen, and Maurice Mulvenna. "Social Network Analysis." International Journal of Ambient Computing and Intelligence 4, no. 3 (2012): 46–58. http://dx.doi.org/10.4018/jaci.2012070104.

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Social Network Analysis is attracting growing attention as social networking sites and their enabled applications transform and impact society. This paper aims to provide a comprehensive review of social network analysis state of the art research and practice. In the paper the authors’ first examine social networking and the core concepts and ingredients of social network analysis. Secondly, they review the trend of social networking and related research. The authors’ then consider modelling motivations, discussing models in line with tie formation approaches, where connections between nodes are taken into account. The authors’ outline data collection approaches along with the common structural properties observed in related literature. They then discuss future directions and the emerging approaches in social network analysis research, notably semantic social networks and social interaction analysis.

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Yang, Hong Mei, Chun Ying Zhang, Rui Tao Liang, and Fang Tian. "Set Pair Social Network Analysis Model." Applied Mechanics and Materials 50-51 (February 2011): 63–67. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.63.

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Through the study on social network information, this paper explore that there exists the certain and uncertain phenomena in the process of finding the relationship between individuals by using social networks, and the social networks are constantly changing. In light of there are some uncertainty and dynamic problems for the network, this paper put forward the set pair social network analysis model and set pair social network analysis model and its properties.

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AYDIN, Nursen. "Social Network Analysis: Literature Review." AJIT-e Online Academic Journal of Information Technology 9, no. 34 (2018): 73–80. http://dx.doi.org/10.5824/1309-1581.2018.4.005.x.

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In this article, social network analysis SNA is defined and historical development process is explained. A comprehensive literature search has been conducted for this purpose. SAA is a powerful method that centralizes individuals and their relations, in that the effect of the individual on the social network can be uncovered and the network of individual groups can be evaluated holistically. SNA shows the structural gaps and social capital in institutions, and focuses managers' attention on critical informal networks. Evaluating strategically important networks within an organization, make "invisible" groups visible in the interaction and allows them to work with key groups to facilitate effective collaboration.

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Linke, Knut, and Torben Friedrich. "Analysis of B-2-C Social Media Communication in Germany." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 3, no. 3 (2018): 23–31. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.33.3002.

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In this research paper, social media marketing activities of social media accounts from German business are analyzed for the social networks Facebook, Twitter, Instagram, Xing and LinkedIn. As research objects were the context of the interaction, the used and targeted social network functionalities and the behavior of the companies selected. The selection of social media accounts for the research included companies which are currently member from the stock market indices DAX and MDAX, additional online and offline retail business, successful German sports clubs, celebrities and others businesses. Also and to be sure to evaluate high-class social media marketing, the German social media award winners from 2015-2017 were analyzed. Out of the results of those two analyses, the results were derivate. The results display different usage approaches between the researched networks and the business fields. As result of the research, several contexts approaches for social media posts are defined. The results contain suggestions for the standardization of those contexts and the different approaches how functions from social networks can be used for user interactions. That includes the targeted reactions and standardized reactions of user interactions.

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MISS, SUPRIYA KUMARI BISWAL, and MRS. PRADNYA MULEY PROF. "DATA MINING IN SOCIAL NETWORK ANALYSIS." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 5 (2018): 9–12. https://doi.org/10.5281/zenodo.1445979.

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<strong>online communities and social software we can communicate with the web. Analysis of interactions happening is complex. Social network analysis (SNA) is a useful way to analyse . Data mining uses different type of statistical,machine learning and graphic al methods and differentiates into a form of knowledge that is useful for most real - world applications. Analysis of social networks is now a popular field for research because it is useful for most applications. In this paper we have discussed the various data mining techniques used for social network analysis.</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141276</strong>

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Jullien, Eva. "Netzwerkanalyse in der Mediävistik. Probleme und Perspektiven im Umgang mit mittelalterlichen Quellen." Vierteljahrschrift für Sozial- und Wirtschaftsgeschichte 100, no. 2 (2013): 135–53. http://dx.doi.org/10.25162/vswg-2013-0004.

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Suh, Wonseok, and Won sug Shin. ""An analysis of discussion environment and group size in online discussion activities using Social Networking Analysis"." Journal of Educational Technology 28, no. 4 (2012): 757–79. http://dx.doi.org/10.17232/kset.28.4.757.

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Gomes Franco e Silva, Flávia. "El uso periodístico de las redes sociales: análisis comparativo entre Brasil y España." aDResearch ESIC International Journal of Communication Research 09, no. 09 (2014): 22–43. http://dx.doi.org/10.7263/adresic-009-02.

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S, Santhosh Kumar, Vishnu Vardhan S, Wasim Jaffar M, Sultan Saleem A, and Sharmasth Vali Y. "Social Communicative Extraction Analysis." International Research Journal of Multidisciplinary Technovation 2, no. 4 (2020): 4–10. http://dx.doi.org/10.34256/irjmt2042.

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The distinguishing proof of online networking networks has as of late been of significant worry, since clients taking an interest in such networks can add to viral showcasing efforts. Right now center around clients' correspondence considering character as a key trademark for recognizing informative systems for example systems with high data streams. We portray the Twitter Personality based Communicative Communities Extraction (T-PCCE) framework that recognizes the most informative networks in a Twitter organize chart thinking about clients' character. We at that point grow existing methodologies as a part of client’s character extraction by collecting information that speak to a few parts of client conduct utilizing AI strategies. We utilize a current measured quality based network discovery calculation and we expand it by embeddings a post-preparing step that dispenses with diagram edges dependent on clients' character. The adequacy of our methodology is exhibited by testing the Twitter diagram and looking at the correspondence quality of the removed networks with and without considering the character factor. We characterize a few measurements to tally the quality of correspondence inside every network. Our algorithmic system and the resulting usage utilize the cloud foundation and utilize the MapReduce Programming Environment. Our outcomes show that the T-PCCE framework makes the most informative networks.

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Skvoretz, John. "Pas de Deux: Social Networks and Network Analysis." Contemporary Sociology: A Journal of Reviews 37, no. 5 (2008): 423–26. http://dx.doi.org/10.1177/009430610803700511.

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Madhavi M. Kulkarni. "Enhancing Social Network Analysis using Graph Neural Networks." Advances in Nonlinear Variational Inequalities 27, no. 4 (2024): 213–30. http://dx.doi.org/10.52783/anvi.v27.1502.

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Social Network Analysis (SNA) may be a key apparatus for figuring out how individuals in social systems interface and relate to each other. Most of the time, chart hypothesis, factual models, and machine learning are utilized in conventional SNA strategies. Be that as it may, these strategies have inconvenience finding complex designs in huge, changing, and assorted systems. Chart Neural Systems (GNNs) are a modern and solid way to progress SNA. They learn models straight from graph-structured information, which makes them exceptionally great at assignments like finding communities, classifying hubs, and foreseeing joins. This think about looks into how GNNs can be utilized to form SNA way better. In specific, conversation approximately how GNN plans like Chart Attention Networks (GATs), Chart Convolutional Systems (GCNs), and GraphSAGE can be utilized to induce both nearby and worldwide structure information from social systems. By utilizing profound learning to combine information from a node's neighbors, GNNs make wealthy include embeddings that keep critical social forms like how impact spreads, how communities are organized, and how solid connections are. GNNs can too handle the sparsity and commotion that are common in social systems well, which makes inquire about more dependable. Conversation almost how combining GNNs with common SNA measurements (like centrality and clustering coefficients) can make organize patterns easier to get it and clarify. By utilizing GNNs on real-life social arrange data, appear that they are more precise at making forecasts and can be utilized on a bigger scale than conventional SNA strategies. The ponder looks at the computing challenges and trade-offs of utilizing GNNs in huge social systems. It talks almost issues like overfitting, show complexity, and being able to get it the models. GNNs are a huge step forward for SNA since they offer assistance us get it social frameworks and connections in a more complex way. Their utilize opens up other ways to see at complicated social occasions, which makes a difference individuals make way better choices in zones like promoting, criticism frameworks, and the spread of data.

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Himelboim, Itai, Marc A. Smith, Lee Rainie, Ben Shneiderman, and Camila Espina. "Classifying Twitter Topic-Networks Using Social Network Analysis." Social Media + Society 3, no. 1 (2017): 205630511769154. http://dx.doi.org/10.1177/2056305117691545.

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As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network structures by segmenting 60 Twitter topical social media network datasets into these six distinct patterns of collective connections, illustrating how different topics of conversations exhibit different patterns of information flow. We discuss conceptual and practical implications for each structure.

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Schwartz, Daniel M., and Tony (D.A.) Rouselle. "Using social network analysis to target criminal networks." Trends in Organized Crime 12, no. 2 (2008): 188–207. http://dx.doi.org/10.1007/s12117-008-9046-9.

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Shin, Sung-Ho, Seungphil Lee, and So Hee Lee. "Analysis of Domestic and International Research Trends using Social Network Analysis in the Field of Trade." Journal of Korea Research Association of International Commerce 23, no. 5 (2023): 31–50. http://dx.doi.org/10.29331/jkraic.2023.10.23.5.31.

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Andryani, Ria, Edi Surya Negara, Rezki Syaputra, and Deni Erlansyah. "Analysis of Academic Social Networks in Indonesia." Qubahan Academic Journal 3, no. 4 (2023): 409–21. http://dx.doi.org/10.58429/qaj.v3n4a289.

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Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.

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Arif, Tasleem. "The Mathematics of Social Network Analysis: Metrics for Academic Social Networks." International Journal of Computer Applications Technology and Research 4, no. 12 (2015): 889–93. http://dx.doi.org/10.7753/ijcatr0412.1003.

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Kim, Jooho, and Makarand Hastak. "Social network analysis: Characteristics of online social networks after a disaster." International Journal of Information Management 38, no. 1 (2018): 86–96. http://dx.doi.org/10.1016/j.ijinfomgt.2017.08.003.

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Zenkovich, K., T. Zhylkybayev, S. Kaysanov, and T. Ustinova. "APPLYING SOCIAL MINING RESULTS FROM OPEN SOCIAL NETWORKS." Bulletin of Shakarim University. Technical Sciences 1, no. 2(14) (2024): 5–10. http://dx.doi.org/10.53360/2788-7995-2024-2(14)-1.

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TThe advent of web-based communities and social networking sites has resulted in a massive amount of social networking data that is embedded with rich sets of meaningful social media knowledge. Social network analysis and the study of social structures using networks and graph theory help to find a systematic method or process for studying social networks. The article reveals the concept of intellectual analysis of social networks. The key aspect of the article is the application of the results of social network analysis to various branches of human activity.Describes the benefits of using Social Mining to identify patterns in big data. Using Social Mining mechanisms, you can find non-trivial and, at first glance, non-obvious patterns in large volumes of information. The article provides examples of software that can be used to quickly collect and analyze data from social networks. Analytics services simplify work and increase opportunities on social networks. Social network analysis provides an effective system for discovering and interpreting online social connections.Social network analytics goes beyond counting likes, reposts and links. This is a comprehensive indepth data analysis that helps to understand what attracts more attention or guide users when accessing the brand through social networks.

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Choi, Mingee, Ji Young Park та Hyun Min Hong. "국내 사회적경제 연구 동향 분석: 텍스트 네트워크, 토픽 모델링 분석기법의 활용한 시기별 핵심 키워드 비교". Center for Social Welfare Research Yonsei University 73 (30 червня 2022): 155–81. http://dx.doi.org/10.17997/swry.73.1.6.

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The purpose of this study is to examine the domestic research trends of social economy and to suggest the direction of future research using text networks and topic modeling analysis techniques. In this study, a total of 3,500 academic papers suitable for the research purpose registered in the Korean Journal Yongin Index(KCI) were set as analysis targets, and core keywords and research topics were compared and analyzed by major period.. As a result of the study, the words that appear in common across time were ‘Responsibility’, ‘Region’, ‘Entrepreneur’, ‘Service’, and ‘Consumer’. Since 2007, new words such as ‘capital’, ‘government’, ‘policy’, ‘system’, ‘management’, and ‘brand’ have emerged in common, which can be interpreted as the result of revitalization of related research as social economy-related legislation progresses.. This study has theoretical implications in that it deepened the knowledge base on domestic social economy research trends by analyzing vast amounts of data processing using text mining techniques.

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Tekşen, Kerem, and Necati Cemaloğlu. "Mobbing and Social Network Analysis." Technium Social Sciences Journal 39 (January 8, 2023): 184–94. http://dx.doi.org/10.47577/tssj.v39i1.8214.

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The aim of this research is to find out the level of mobbing experience of teachers working in educational institutions and to determine the network characteristics of both the social networks of the organizations where mobbing behavior is common and the participants in these networks. The target population of the research consists of teachers working in a province in Türkiye. The sample of the population was determined by cluster sampling method. In total, 376 teachers in 30 schools were reached, but 11 questionnaires were removed during the pre-analysis data scanning phase, and the remaining 365 questionnaires were analyzed. “Negative Acts Questionnaire” and “Social Network Analysis Questionnaire” were used as data collection tools in the research. SPSS 21.0 and UCINET 6 statistical package programs were used for the analysis of the data obtained in the research, and "frequency", "mean" and "multi-network measurements" were used in data analysis. As a result of the research, it is determined that the average level of mobbing experience of teachers in the organizations participating in the research is low. In addition, three organizations where mobbing is common is determined and the social network structures of these organizations is examined. It is observed that the average degrees and network densities are generally low in these organizations. In addition, these organizations generally show a low level of transitivity. In addition, it is evaluated that some of the participants in the social networks of these organizations may be victims of mobbing, considering that they have a low overall degree. As a support to this finding, it is observed that the participants in question have higher internal and external closeness, low betweenness and low eigenvector values compared to other participants in the organization.

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Jazmin, Enriquez-Sanchez, Munoz-Rodriguez Manrrubio, J. Reyes Altamirano-Cardenas, and Gante Abraham Villegas-De. "Activation process analysis of the Localized Agri-food System using social networks." Agricultural Economics (Zemědělská ekonomika) 63, No. 3 (2017): 121–35. http://dx.doi.org/10.17221/254/2015-agricecon.

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The objective of the work was to analyse the prevailing activation process of the Localized Agri-food System (LAS) by using social networks as a tool to value the pre-existing social capital. There were 27 producers of “Chiapas Cream Cheese” and the members of the formal cheese maker organization from the state of Chiapas, Mexico that were interviewed. By the means of cluster analysis and the graphic design of friendship, the kinship, the “compadrazgo” knowledge, the collaboration and cooperation networks, we concluded that the structural activation must transcend the formal creation of an organization. It is best to value and then mobilize the pre-existing social capital in a territory with a specific traditional know-how as a foundation to the structure and activation process of the LAS. Four actors were identified for their active participation in all analysed networks; these were the information diffusers and network structures. Weak links in the cheese maker organization favour the innovation adoption; whereas the strong links maintain the know-how.

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Brass, Daniel J. "New Developments in Social Network Analysis." Annual Review of Organizational Psychology and Organizational Behavior 9, no. 1 (2022): 225–46. http://dx.doi.org/10.1146/annurev-orgpsych-012420-090628.

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This review of social network analysis focuses on identifying recent trends in interpersonal social networks research in organizations, and generating new research directions, with an emphasis on conceptual foundations. It is organized around two broad social network topics: structural holes and brokerage and the nature of ties. New research directions include adding affect, behavior, and cognition to the traditional structural analysis of social networks, adopting an alter-centric perspective including a relational approach to ego and alters, moving beyond the triad in structural hole and brokerage research to consider alters as brokers, expanding the nature of ties to include negative, multiplex/dissonant, and dormant ties, and exploring the value of redundant ties. The challenge is to answer the question “What's next in social network analysis?”

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Yu Kyung Kim, Yu Kyung Kim, Myong Hyun Go Myong Hyun Go, Sonyong Kim Sonyong Kim, Jaeyeon Lee Jaeyeon Lee, and Kyungho Lee Kyungho Lee. "Evaluating Cybersecurity Capacity Building of ASEAN Plus Three through Social Network Analysis." 網際網路技術學刊 24, no. 2 (2023): 495–505. http://dx.doi.org/10.53106/160792642023032402031.

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The strategic rivalry between the United States and China is out in full swing in Southeast Asia. As the result, ASEAN is emerging both as a key player as well as a playground for pivotal global and regional actors. South Korea, Japan, and China known as ASEAN Plus Three, have strong economic, political, and technological ties with the region, and have leveraged their cyber capabilities to compete for influence in the region. This study evaluates the relative performances of the Plus Three&rsquo; cyber outreach efforts to the region by visualizing the complex web of actors and cyber cooperation and assistance activities with network analysis tools and open-source databases. We quantitatively analyze national cyber security cooperation of ASEAN including South Korea, Japan, and China through capacity building indicators and social network methodology. This study (1) analyzes cybersecurity cooperation in ASEAN Plus Three, (2) explores factors that influence cooperation, and (3) lays out a quantitative basis for establishing national information policy and cybersecurity strategy. We find that the Plus Three, despite the outward similarity in their respective regional strategies, are a study of contrasts, with one of them emerging as an influential yet silent power in the regional cyber diplomacy domain.

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Lu, Yingjie, Xinwei Wang, Lin Su, and Han Zhao. "Multiplex Social Network Analysis to Understand the Social Engagement of Patients in Online Health Communities." Mathematics 11, no. 21 (2023): 4412. http://dx.doi.org/10.3390/math11214412.

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Social network analysis has been widely used in various fields including online health communities. However, it is still a challenge to understand how patients’ individual characteristics and online behaviors impact the formation of online health social networks. Furthermore, patients discuss various health topics and form multiplex social networks covering different aspects of their illnesses, including symptoms, treatment experiences, resource sharing, emotional expression, and new friendships. Further research is needed to investigate whether the factors influencing the formation of these topic-based networks are different and explore potential interconnections between various types of social relationships in these networks. To address these issues, this study applied exponential random graph models to characterize multiplex health social networks and conducted empirical research in a Chinese online mental health community. An integrated social network and five separate health-related topic-specific networks were constructed, each with 773 users as network nodes. The empirical findings revealed that patients’ demographic attributes (e.g., age, gender) and online behavioral features (e.g., emotional expression, online influence, participation duration) have significant impacts on the formation of online health social networks, and these patient characteristics have significantly different effects on various types of social relationships within multiplex networks. Additionally, significant cross-network effects, including entrainment and exchange effects, were found among multiple health topic-specific networks, indicating strong interdependencies between them. This research provides theoretical contributions to social network analysis and practical insights for the development of online healthcare social networks.

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Karthika, S., and S. Bose. "A COMPARATIVE STUDY OF SOCIAL NETWORKING APPROACHES IN IDENTIFYING THE COVERT NODES." International Journal on Web Service Computing (IJWSC) 2, no. 3 (2011): 65–78. https://doi.org/10.5281/zenodo.4021830.

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This paper categories and compares various works done in the field of social networking for covert networks. It uses criminal network analysis to categorize various approaches in social engineering like dynamic network analysis, destabilizing covert networks, counter terrorism, key player, subgroup detection and homeland security. The terrorist network has been taken for study because of its network of individuals who spread from continents to continents and have an effective influence of their ideology throughout the globe. It also presents various metrics based on which the centrality of nodes in the graphs could be identified and it’s illustrated based on a synthetic dataset for 9/11 attack. This paper will also discuss various open problems in this area.

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Karthika, S., and S. Bose. "A COMPARATIVE STUDY OF SOCIAL NETWORKING APPROACHES IN IDENTIFYING THE COVERT NODES." International Journal on Web Service Computing (IJWSC) 2, no. 3 (2011): 65–78. https://doi.org/10.5281/zenodo.3385671.

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This paper categories and compares various works done in the field of social networking for covert networks. It uses criminal network analysis to categorize various approaches in social engineering like dynamic network analysis, destabilizing covert networks, counter terrorism, key player, subgroup detection and homeland security. The terrorist network has been taken for study because of its network of individuals who spread from continents to continents and have an effective influence of their ideology throughout the globe. It also presents various metrics based on which the centrality of nodes in the graphs could be identified and it’s illustrated based on a synthetic dataset for 9/11 attack. This paper will also discuss various open problems in this area.

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Bharsakle, Pragati Dnyaneshwar. "Social Networks for Threat Perception and Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 5073–77. http://dx.doi.org/10.22214/ijraset.2021.35911.

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In the current era of massive knowledge, high volumes of valuable knowledge is simply collected and generated. Social networks square measure samples of generating sources of those huge knowledge. Users in these social networks square measure usually coupled by some interdependency like friendly relationship. As these huge social networks continue to grow, there square measure things during which Associate in Nursing individual user needs to seek out common teams of friends so he will suggest a similar teams to alternative users. Many users of social Network are not aware about the number of security risks in networks such as identity theft, privacy violations, sexual harassment etc,. Recent studies says that most of the social network users expose their personal information like their date of birth, email address, phone number, relationship status. If this type of data reached to the wrong person, then person used that information to harm the users. If the children are users of social network, then these risks become serious. In this paper we present an alternative data analytic solution by using pattern matching solution.

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Rowley, Timothy J. "Social Network Analysis." Proceedings of the International Association for Business and Society 7 (1996): 999–1009. http://dx.doi.org/10.5840/iabsproc1996794.

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S., Sukharev O., and Kurmanov N.V. "Social Network Analysis." Advances in Economics and Business 2, no. 3 (2014): 121–26. http://dx.doi.org/10.13189/aeb.2014.020301.

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Jain, Susha, Mahaveer Jain, and Balasubramani R. "Social Network Analysis." IJARCCE 8, no. 5 (2019): 236–40. http://dx.doi.org/10.17148/ijarcce.2019.8543.

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Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.

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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.

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Scott, John. "Social Network Analysis." Sociology 22, no. 1 (1988): 109–27. http://dx.doi.org/10.1177/0038038588022001007.

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Streeter, Calvin L., and David F. Gillespie. "Social Network Analysis." Journal of Social Service Research 16, no. 1-2 (1993): 201–22. http://dx.doi.org/10.1300/j079v16n01_10.

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Buštíková, Lenka. "Social Network Analysis." Czech Sociological Review 35, no. 2 (1999): 193–206. http://dx.doi.org/10.13060/00380288.1999.35.2.10.

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Comunian, Roberta. "Social network analysis." Regional Insights 2, no. 2 (2011): 3. http://dx.doi.org/10.1080/20429843.2011.9727917.

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Sweeney, Patricia M., Elizabeth F. Bjerke, Hasan Guclu, et al. "Social Network Analysis." Journal of Public Health Management and Practice 19, no. 6 (2013): E38—E40. http://dx.doi.org/10.1097/phh.0b013e31829fc013.

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