Добірка наукової літератури з теми "Graph, social and multimedia data"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Graph, social and multimedia data".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Graph, social and multimedia data"
Wagenpfeil, Stefan, Binh Vu, Paul Mc Kevitt, and Matthias Hemmje. "Fast and Effective Retrieval for Large Multimedia Collections." Big Data and Cognitive Computing 5, no. 3 (July 22, 2021): 33. http://dx.doi.org/10.3390/bdcc5030033.
Повний текст джерелаXu, Zheng, Zhiguo Yan, Yunhuai Liu, and Lin Mei. "Measuring the Semantic Relatedness Between Images Using Social Tags." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 2 (April 2013): 1–12. http://dx.doi.org/10.4018/ijcini.2013040101.
Повний текст джерелаXu, Zheng, Xiangfeng Luo, Yunhuai Liu, Lin Mei, and Chuanping Hu. "Measuring Semantic Relatedness between Flickr Images: From a Social Tag Based View." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/758089.
Повний текст джерелаMozhaiev, Mykhailo, and Pavlo Buslov. "METHOD OF MODELING OF A SOCIAL PROFILE USING BIG DATA STRUCTURE TRANSFORMATION OPTIMIZATION." Advanced Information Systems 5, no. 1 (June 22, 2021): 12–17. http://dx.doi.org/10.20998/2522-9052.2021.1.02.
Повний текст джерелаLiu, Yan, and Shuo Zhu. "Multimodal Wireless Situational Awareness-Based Tourism Service Scene." Journal of Sensors 2021 (December 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/5503333.
Повний текст джерелаSakurai, Keigo, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement Learning." Sensors 22, no. 10 (May 13, 2022): 3722. http://dx.doi.org/10.3390/s22103722.
Повний текст джерелаZhang, Mingliang, Xiangyang Luo, Pei Zhang, Hao Li, Yi Zhang, and Lingling Li. "High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things." Security and Communication Networks 2021 (December 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/6351144.
Повний текст джерелаDabhade, Kiran Bhimrao, and C. M. Mankar. "An Optimization of Adaptive Computing-plus-Communication for Multimedia Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 623–28. http://dx.doi.org/10.22214/ijraset.2022.47294.
Повний текст джерелаAgosti, Maristella, Maurizio Atzori, Paolo Ciaccia, and Letizia Tanca. "Report on SEBD 2020." ACM SIGIR Forum 54, no. 2 (December 2020): 1–5. http://dx.doi.org/10.1145/3483382.3483392.
Повний текст джерелаHou, Li, Qi Liu, Mueen Uddin, Hizbullah Khattak, and Muhammad Asshad. "Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data." Mobile Information Systems 2021 (September 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/8396771.
Повний текст джерелаДисертації з теми "Graph, social and multimedia data"
Kim, Pilho. "E-model event-based graph data model theory and implementation /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29608.
Повний текст джерелаCommittee Chair: Madisetti, Vijay; Committee Member: Jayant, Nikil; Committee Member: Lee, Chin-Hui; Committee Member: Ramachandran, Umakishore; Committee Member: Yalamanchili, Sudhakar. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Wang, Guan. "Graph-Based Approach on Social Data Mining." Thesis, University of Illinois at Chicago, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668648.
Повний текст джерелаPowered by big data infrastructures, social network platforms are gathering data on many aspects of our daily lives. The online social world is reflecting our physical world in an increasingly detailed way by collecting people's individual biographies and their various of relationships with other people. Although massive amount of social data has been gathered, an urgent challenge remain unsolved, which is to discover meaningful knowledge that can empower the social platforms to really understand their users from different perspectives.
Motivated by this trend, my research addresses the reasoning and mathematical modeling behind interesting phenomena on social networks. Proposing graph based data mining framework regarding to heterogeneous data sources is the major goal of my research. The algorithms, by design, utilize graph structure with heterogeneous link and node features to creatively represent social networks' basic structures and phenomena on top of them.
The graph based heterogeneous mining methodology is proved to be effective on a series of knowledge discovery topics, including network structure and macro social pattern mining such as magnet community detection (87), social influence propagation and social similarity mining (85), and spam detection (86). The future work is to consider dynamic relation on social data mining and how graph based approaches adapt from the new situations.
Wong, León Kevin, and Valdivia Diego Eduardo Antonio Rodríguez. "Distributed Social Media System - Multimedia Data Linkage." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2014. http://hdl.handle.net/10757/324525.
Повний текст джерелаTesis
Bracamonte, Nole Teresa Jacqueline. "Improving web multimedia information retrieval using social data." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168681.
Повний текст джерелаBuscar contenido multimedia es una de las tareas más comunes que los usuarios realizan en la Web. Actualmente, los motores de búsqueda en la Web han mejorado la precisión de sus búsquedas de contenido multimedia y ahora brindan una mejor experiencia de usuarios. Sin embargo, estos motores aún no logran obtener resultados precisos para consultas que no son comunes, y consultas que se refieren a conceptos abstractos. En ambos escenarios, la razón principal es la falta de información preliminar. Esta tesis se enfoca en mejorar la recuperación de información multimedia en la Web usando datos generados a partir de la interacción entre usuarios y recursos multimedia. Para eso, se propone mejorar la recuperación de información multimedia desde dos perspectivas: (1) extrayendo conceptos relevantes a los recursos multimedia, y (2) mejorando las descripciones multimedia con datos generados por el usuario. En ambos casos, proponemos sistemas que funcionan independientemente del tipo de multimedia, y del idioma de los datos de entrada. En cuanto a la identificación de conceptos relacionados a objetos multimedia, desarrollamos un sistema que va desde los resultados de búsqueda específicos de la consulta hasta los conceptos detectados para dicha consulta. Nuestro enfoque demuestra que podemos aprovechar la vista parcial de una gran colección de documentos multimedia para detectar conceptos relevantes para una consulta determinada. Además, diseñamos una evaluación basada en usuarios que demuestra que nuestro algoritmo de detección de conceptos es más sólido que otros enfoques similares basados en detección de comunidades. Para mejorar la descripción multimedia, desarrollamos un sistema que combina contenido audio-visual de documentos multimedia con información de su contexto para mejorar y generar nuevas anotaciones para los documentos multimedia. Específicamente, extraemos datos de clicks de los registros de consultas y usamos las consultas como sustitutos para las anotaciones manuales. Tras una primera inspección, demostramos que las consultas proporcionan una descripción concisa de los documentos multimedia. El objetivo principal de esta tesis es demostrar la relevancia del contexto asociado a documentos multimedia para mejorar el proceso de recuperación de documentos multimedia en la Web. Además, mostramos que los grafos proporcionan una forma natural de modelar problemas multimedia.
Fondef D09I-1185, CONICYT-PCHA/Doctorado Nacional/2013-63130260, Apoyo a estadías corta de la Escuela de Postgrado de la U. de Chile, y el Núcleo Milenio CIWS
Hassanzadeh, Reza. "Anomaly detection in online social networks : using data-mining techniques and fuzzy logic." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78679/1/Reza_Hassanzadeh_Thesis.pdf.
Повний текст джерелаMaryokhin, Tymur. "Data dissemination in large-cardinality social graphs." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268.
Повний текст джерелаCasas, Roma Jordi. "Privacy-preserving and data utility in graph mining." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/285566.
Повний текст джерелаIn recent years, an explosive increase of graph-formatted data has been made publicly available. Embedded within this data there is private information about users who appear in it. Therefore, data owners must respect the privacy of users before releasing datasets to third parties. In this scenario, anonymization processes become an important concern. However, anonymization processes usually introduce some kind of noise in the anonymous data, altering the data and also their results on graph mining processes. Generally, the higher the privacy, the larger the noise. Thus, data utility is an important factor to consider in anonymization processes. The necessary trade-off between data privacy and data utility can be reached by using measures and metrics to lead the anonymization process to minimize the information loss, and therefore, to maximize the data utility. In this thesis we have covered the fields of user's privacy-preserving in social networks and the utility and quality of the released data. A trade-off between both fields is a critical point to achieve good anonymization methods for the subsequent graph mining processes. Part of this thesis has focused on data utility and information loss. Firstly, we have studied the relation between the generic information loss measures and the clustering-specific ones, in order to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes. We have found strong correlation between some generic information loss measures (average distance, betweenness centrality, closeness centrality, edge intersection, clustering coefficient and transitivity) and the precision index over the results of several clustering algorithms, demonstrating that these measures are able to predict the perturbation introduced in anonymous data. Secondly, two measures to reduce the information loss on graph modification processes have been presented. The first one, Edge neighbourhood centrality, is based on information flow throw 1-neighbourhood of a specific edge in the graph. The second one is based on the core number sequence and it preserves better the underlying graph structure, retaining more data utility. By an extensive experimental set up, we have demonstrated that both methods are able to preserve the most important edges in the network, keeping the basic structural and spectral properties close to the original ones. The other important topic of this thesis has been privacy-preserving methods. We have presented our random-based algorithm, which utilizes the concept of Edge neighbourhood centrality to drive the edge modification process to better preserve the most important edges in the graph, achieving lower information loss and higher data utility on the released data. Our method obtains a better trade-off between data utility and data privacy than other methods. Finally, two different approaches for k-degree anonymity on graphs have been developed. First, an algorithm based on evolutionary computing has been presented and tested on different small and medium real networks. Although this method allows us to fulfil the desired privacy level, it presents two main drawbacks: the information loss is quite large in some graph structural properties and it is not fast enough to work with large networks. Therefore, a second algorithm has been presented, which uses the univariate micro-aggregation to anonymize the degree sequence and reduce the distance from the original one. This method is quasi-optimal and it results in lower information loss and better data utility.
Rossi, Maria. "Graph Mining for Influence Maximization in Social Networks." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX083/document.
Повний текст джерелаModern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures
Zulfiqar, Omer. "Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103745.
Повний текст джерелаMaster of Science
Millions of people worldwide rely on public transit networks for their daily commutes and day to day movements. With the growth in the number of people using the service, there has been an increase in the number of daily passengers affected by service disruptions. This thesis and research involves proposing and developing three different approaches to help aid in the timely detection of these disruptions. In this work we have developed a pure data mining approach along with two deep learning models using neural networks and live data from Twitter to identify these disruptions. The data mining approach uses a set of dirsuption related input keywords to identify similar keywords within the live Twitter data. By collecting historical data we were able to create deep learning models that represent the vocabulary from the disruptions related Tweets in the form of a graph. A graph is a collection of data values where the data points are connected to one another based on their relationships. A longer chain of connection between two words defines a weak relationship, a shorter chain defines a stronger relationship. In our graph, words with similar contextual meanings are connected to each other over shorter distances, compared to words with different meanings. At the end we use a neural network as a classifier to scan this graph to learn the semantic relationships within our data. Afterwards, this learned information can be used to accurately classify the disruption related Tweets within a pool of random Tweets. Once all the proposed approaches have been developed, a benchmark evaluation is performed against other existing text classification techniques, to justify the effectiveness of the approaches. The final results indicate that the proposed graph based models achieved a higher accuracy, compared to the data mining model, and also outperformed all the other baseline models. Our Tweet-Level GCN had the highest accuracy of 89.9%.
Dos, Santos Raimundo Fonseca Jr. "Effective Methods of Semantic Analysis in Spatial Contexts." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/49697.
Повний текст джерелаPh. D.
Книги з теми "Graph, social and multimedia data"
Florian, Alt, Michelis Daniel, and SpringerLink (Online service), eds. Pervasive Advertising. London: Springer-Verlag London Limited, 2011.
Знайти повний текст джерелаPoland 2004) International Workshop on Intelligent Media Technology for Communicative Intelligence (2nd Warsaw. International Workshop on Intelligent Media Technology for Communicative Intelligence: Warsaw, Poland, September 13-14, 2004 : proceedings. Warsaw: PJIIT (Polish-Japanese Institute of Information Technology) Publishing House, 2004.
Знайти повний текст джерелаT, Henry Gary, and American Evaluation Association, eds. Creating effective graphs: Solutions for a variety of evaluation data. San Franciso, Calif: Jossey-Bass Publishers, 1997.
Знайти повний текст джерелаBarrett, Edward. The Society of text: Hypertext, hypermedia, and the social construction of information. Cambridge, Mass: MIT Press, 1989.
Знайти повний текст джерелаservice), SpringerLink (Online, ed. Handbook of Social Network Technologies and Applications. Boston, MA: Springer Science+Business Media, LLC, 2010.
Знайти повний текст джерелаFacebook nation: Total information awareness. New York, N.Y: Springer, 2013.
Знайти повний текст джерелаMorselli, Carlo. Inside criminal networks. New York: Springer Science+Business Media, 2009.
Знайти повний текст джерелаShu zi mei jie xia de wen yi zhuan xing: Literature and art transformation under the digital medium. Beijing: Zhongguo she hui ke xue chu ban she, 2011.
Знайти повний текст джерелаRamzan, Naeem. Social Media Retrieval. London: Springer London, 2013.
Знайти повний текст джерелаVincent A. W. M. M. Aleven. Intelligent Tutoring Systems: 10th International Conference, ITS 2010, Pittsburgh, PA, USA, June 14-18, 2010, Proceedings, Part I. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.
Знайти повний текст джерелаЧастини книг з теми "Graph, social and multimedia data"
Moscato, Vincenzo, Antonio Picariello, and Giancarlo Sperlí. "An Hypergraph Data Model for Expert Finding in Multimedia Social Networks." In Graph-Based Representations in Pattern Recognition, 110–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20081-7_11.
Повний текст джерелаNguyen, Manh-Duy, Binh T. Nguyen, and Cathal Gurrin. "Graph-Based Indexing and Retrieval of Lifelog Data." In MultiMedia Modeling, 256–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67835-7_22.
Повний текст джерелаMarcus, Sherry E., Melanie Moy, and Thayne Coffman. "Social Network Analysis." In Mining Graph Data, 443–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch17.
Повний текст джерелаRoy, Suman Deb, and Wenjun Zeng. "Revelations from Social Multimedia Data." In Social Multimedia Signals, 135–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09117-4_10.
Повний текст джерелаRoy, Suman Deb, and Wenjun Zeng. "Data Visualization: Gazing at Ripples." In Social Multimedia Signals, 161–74. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09117-4_12.
Повний текст джерелаWang, Jingdong, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, and Baining Guo. "Fast Neighborhood Graph Search Using Cartesian Concatenation." In Multimedia Data Mining and Analytics, 397–417. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_18.
Повний текст джерелаMin, Yong, Yuying Zhou, Tingjun Jiang, and Ye Wu. "Exploring the Controlled Experiment by Social Bots." In Graph Data Mining, 223–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2609-8_11.
Повний текст джерелаBoldi, Paolo, and Sebastiano Vigna. "(Web/Social) Graph Compression." In Encyclopedia of Big Data Technologies, 1–5. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_54-1.
Повний текст джерелаBoldi, Paolo, and Sebastiano Vigna. "(Web/Social) Graph Compression." In Encyclopedia of Big Data Technologies, 1800–1804. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_54.
Повний текст джерелаNiu, Teng, Shiai Zhu, Lei Pang, and Abdulmotaleb El Saddik. "Sentiment Analysis on Multi-View Social Data." In MultiMedia Modeling, 15–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27674-8_2.
Повний текст джерелаТези доповідей конференцій з теми "Graph, social and multimedia data"
Zheng, Jiaqi, Xi Zhang, Sanchuan Guo, Quan Wang, Wenyu Zang, and Yongdong Zhang. "MFAN: Multi-modal Feature-enhanced Attention Networks for Rumor Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/335.
Повний текст джерелаZhuang, Chenyi, Nicholas Jing Yuan, Ruihua Song, Xing Xie, and Qiang Ma. "Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory." 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.2017/506.
Повний текст джерелаBai, Ting, Youjie Zhang, Bin Wu, and Jian-Yun Nie. "Temporal Graph Neural Networks for Social Recommendation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378444.
Повний текст джерелаGulati, Avni, and Magdalini Eirinaki. "Influence Propagation for Social Graph-based Recommendations." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622213.
Повний текст джерелаOnodera, Nozomu, Keisuke Maeda, Takahiro Ogawa, and Miki Haseyama. "Popularity-Aware Graph Social Recommendation for Fully Non-Interaction Users." In MMAsia '22: ACM Multimedia Asia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3551626.3564969.
Повний текст джерелаDas, Pratyusha, and Antonio Ortega. "Graph-based skeleton data compression." In 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2020. http://dx.doi.org/10.1109/mmsp48831.2020.9287103.
Повний текст джерелаSchinas, Manos, Symeon Papadopoulos, Georgios Petkos, Yiannis Kompatsiaris, and Pericles A. Mitkas. "Multimodal Graph-based Event Detection and Summarization in Social Media Streams." In MM '15: ACM Multimedia Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2733373.2809933.
Повний текст джерелаRawashdeh, Majdi, Mohammed F. Alhamid, Heung-Nam Kim, Awny Alnusair, Vanessa Maclsaac, and Abdulmotaleb El Saddik. "Graph-based personalized recommendation in social tagging systems." In 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE, 2014. http://dx.doi.org/10.1109/icmew.2014.6890593.
Повний текст джерелаEfstathiades, Hariton, Demetris Antoniades, George Pallis, Marios D. Dikaiakos, Zoltan Szlavik, and Robert-Jan Sips. "Online social network evolution: Revisiting the Twitter graph." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840655.
Повний текст джерелаAkcora, Cuneyt, Barbara Carminati, and Elena Ferrari. "Privacy in Social Networks: How Risky is Your Social Graph?" In 2012 IEEE International Conference on Data Engineering (ICDE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icde.2012.99.
Повний текст джерелаЗвіти організацій з теми "Graph, social and multimedia data"
Iatsyshyn, Anna V., Iryna H. Hubeladze, Valeriia O. Kovach, Valentyna V. Kovalenko, Volodymyr O. Artemchuk, Maryna S. Dvornyk, Oleksandr O. Popov, Andrii V. Iatsyshyn, and Arnold E. Kiv. Applying digital technologies for work management of young scientists' councils. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4434.
Повний текст джерелаMonetary Policy Report - October 2020. Banco de la República de Colombia, February 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr4.-2020.
Повний текст джерелаMonetary Policy Report - January 2022. Banco de la República, March 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr1-2022.
Повний текст джерела