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Artykuły w czasopismach na temat "Topic evolution networks"
Liu, Yanni, Dongsheng Liu i Yuwei Chen. "Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City". Complexity 2020 (4.06.2020): 1–13. http://dx.doi.org/10.1155/2020/9789431.
Pełny tekst źródłaWang, Xiang Yang. "Hot Topic Detection in News Blog". Applied Mechanics and Materials 513-517 (luty 2014): 1114–18. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1114.
Pełny tekst źródłaJung, Sukhwan, i Aviv Segev. "Analyzing the generalizability of the network-based topic emergence identification method". Semantic Web 13, nr 3 (6.04.2022): 423–39. http://dx.doi.org/10.3233/sw-212951.
Pełny tekst źródłaXu, Xiaoyan, Wei Lv, Beibei Zhang, Shuaipeng Zhou, Wei Wei i Yusen Li. "A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks". Complexity 2021 (26.08.2021): 1–14. http://dx.doi.org/10.1155/2021/8859225.
Pełny tekst źródłaLiang, Wei, Zixian Lu, Qun Jin, Yonghua Xiong i Min Wu. "Modeling and Analyzing of Research Topic Evolution Associated with Social Networks of Researchers". International Journal of Distributed Systems and Technologies 7, nr 3 (lipiec 2016): 42–62. http://dx.doi.org/10.4018/ijdst.2016070103.
Pełny tekst źródłaJensen, Scott, Xiaozhong Liu, Yingying Yu i Staša Milojevic. "Generation of topic evolution trees from heterogeneous bibliographic networks". Journal of Informetrics 10, nr 2 (maj 2016): 606–21. http://dx.doi.org/10.1016/j.joi.2016.04.002.
Pełny tekst źródłaBrunilde Sanso, Lorela Cano i Antonio Capone. "On the evolution of infrastructure sharing in mobile networks: A survey". ITU Journal on Future and Evolving Technologies 1, nr 1 (21.12.2020): 141–57. http://dx.doi.org/10.52953/nbqh9604.
Pełny tekst źródłaLee, Won Sang. "Analyzing the Evolution of Interdisciplinary Areas". Journal of Global Information Management 30, nr 1 (1.01.2022): 1–23. http://dx.doi.org/10.4018/jgim.304062.
Pełny tekst źródłaCai, Meng, Han Luo i Ying Cui. "A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies". Complexity 2021 (2.12.2021): 1–23. http://dx.doi.org/10.1155/2021/2069010.
Pełny tekst źródłaGomez, Manuel J., José A. Ruipérez-Valiente i Félix J. García Clemente. "Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study". Sensors 23, nr 23 (21.11.2023): 9303. http://dx.doi.org/10.3390/s23239303.
Pełny tekst źródłaRozprawy doktorskie na temat "Topic evolution networks"
Li, Ke. "Exploring Topic Evolution in Large Scientific Archives with Pivot Graphs". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS043.
Pełny tekst źródłaThere is an increasing demand for practical tools to explore the evolution of scientific research published in bibliographic archives such as the Web of Science, arXiv, PubMed or ISTEX. Revealing meaningful evolution patterns from these document archives has many applications and can be extended to synthesize narratives from datasets across multiple domains, including news stories, research papers, legal cases and works of literature. In this thesis, we propose a data model and query language for the visualization and exploration of topic evolution networks. Our model is independent of a particular topic extraction and alignment method and proposes a set of semantic and structural metrics for characterizing and filtering meaningful topic evolution patterns. These metrics are particularly useful for the visualization and the exploration of large topic evolution networks. We also present a prototype implementation of our model on top of Apache Spark and experimental results obtained for four representative document archives
Teixeira, Diana Von-Haff Lopes. "Spatio-temporal distribution analysis of brand interest in social networks". Master's thesis, 2018. http://hdl.handle.net/10071/18604.
Pełny tekst źródłaActualmente, plataformas como Twitter e Facebook fazem parte do dia-a-dia de muitas pessoas e são usadas por milhões de utilizadores. Nestas plataformas, denominadas Redes Sociais, os utilizadores partilham informações incluindo opiniões, sentimentos, experiências e pensamentos. A plataforma Twitter, em particular, e usada para partilhar diversos tópicos, que podem incluir dicussões sobre marcas, seus produtos e/ou serviços. O presente estudo analisa como o interesse numa marca e reflectido na Rede Social Twitter e apresenta uma metodologia que permite utilizar o Twitter como fonte de informação para monitorizar o que os utilizadores dizem acerca de determinadas marcas. O interesse numa marca pode ser definido como o nível de interesse que um indivíduo tem por uma marca, e o nível de curiosidade que um indivíduo tem e que o leva a aprender mais acerca dessa marca. Neste estudo, o número de tweets publicados e usado para medir o interesse nas marcas escolhidas. A metodologia seguida baseia-se na data em que o tweet foi publicado, localização, e número de publicações, para efectuar uma análise espacio-temporal. Adicionalmente, apresenta-se uma framework que possibilita a exploração de um vasto conjunto de dados, com o objectivo de revelar padrões latentes, bem como analisar o interesse nas marcas seleccionadas, usando o Twitter como fonte dados. Para o efeito, aplicou-se Topic Modelling, uma técnica de Text Mining bastante utilizada para descobrir tópicos em texto não estruturado. Algoritmos de Topic Modelling têm sido amplamente utilizados para monitorizar eventos e tendências e descobrir tópicos em áreas como educação, marketing, saúde, entre outras. A framework consiste em treinar o modelo de tópicos LDA (Latent Dirichlet Allocation) usando tweets agrupados (considerando determinado critério) e posteriormente aplicar o modelo treinado noutro conjunto de tweets agrupados (considerando outro critério). Descreve-se um conjunto de tarefas de pré-processamento dos dados que ajudaram a melhorar o desempenho dos modelos, a obter melhor resultados e, consequentemente, a efectuar uma melhor análise. As experiências revelam que atravês de Topic Modelling e possível rastrear dicussões de utilizadores de Redes Sociais durante um longo período de tempo, e capturar alterações relacionadas com acontecimentos reais.
Chen, Jia-Yu, i 陳佳瑜. "A Novel Citation Network for Research Topic Evolution". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/d5j5cc.
Pełny tekst źródła國立臺灣大學
資訊管理學研究所
107
In the past, people consumed related research thesis, journal papers or patent specifications to conclude the research trend of a topic area. However, with the coming of information explosion, consuming such a large number of data only by human seems to be inefficient. Therefore, it is important to develop a method to help users observe the evolution of research topics. In this study, we propose a novel citation network for detecting research trend to replace the past citation network that use a single document as a node and the citation relationships between documents as links. We extract the keywords which were specified by authors from the documents and redefine the citation relationships between nodes as well as the weight of edges. After deleting edges whose weight is lower than designated threshold, nodes in the final citation network would be clustered to find the research topics. Each cluster of nodes is considered as a research topic. The details of a research topic can be realized from the keywords in the cluster. Finally, we are able to observe the evolution of a topic area by checking the relationships among research topics in different years. The experiment shows that the proposed novel citation network can extract useful research topics from numerous literature and assist domain experts to observe the change of research topics.
"Topics in dynamical processes in networked objects". 2008. http://library.cuhk.edu.hk/record=b5896855.
Pełny tekst źródłaThesis (M.Phil.)--Chinese University of Hong Kong, 2008.
Includes bibliographical references (leaves 114-118).
Abstracts in English and Chinese.
Lee, Kwan Ho = Tan tao wang luo shang de ruo gan dong tai guo cheng / Li Junhao.
Chapter 1 --- Overview --- p.1
Chapter 2 --- Networks --- p.5
Chapter 2.1 --- Describing Networks --- p.5
Chapter 2.1.1 --- Adjacency Matrix --- p.6
Chapter 2.1.2 --- "Degree, Degree Distribution and Mean Degree" --- p.6
Chapter 2.1.3 --- Clustering Coefficient --- p.7
Chapter 2.1.4 --- "Shortest Path, Shortest Distance and Diameter" --- p.7
Chapter 2.1.5 --- Betweenness --- p.9
Chapter 2.2 --- Barabasi-Albert (BA) Network --- p.10
Chapter 2.2.1 --- Construction of BA Network --- p.10
Chapter 2.2.2 --- Analytical Study of Degree Distribution --- p.11
Chapter 2.2.3 --- Numerical Study of Degree Distribution --- p.12
Chapter 2.2.4 --- Shortest Distance --- p.13
Chapter 2.3 --- Summary --- p.14
Chapter 3 --- Routing in Networks: A Review --- p.16
Chapter 3.1 --- Introduction --- p.16
Chapter 3.2 --- Dijkstra´ةs Algorithm --- p.17
Chapter 3.2.1 --- Algorithm --- p.17
Chapter 3.2.2 --- Running Time --- p.18
Chapter 3.2.3 --- Routing Table Based on Shortest Path Algorithm --- p.19
Chapter 3.3 --- Routing Model --- p.19
Chapter 3.3.1 --- General Setup --- p.19
Chapter 3.3.2 --- Phase Transition and Evaluation of Network Performance --- p.20
Chapter 3.4 --- Using Shortest Path as Routing Algorithm on BA Networks --- p.21
Chapter 3.5 --- Other Routing Algorithms --- p.22
Chapter 3.5.1 --- Efficient Path --- p.23
Chapter 3.5.2 --- Routing based on Local Structural Information --- p.24
Chapter 3.5.3 --- Routing based on Dynamical Information --- p.25
Chapter 3.6 --- Summary --- p.26
Chapter 4 --- Optimization of Routing Efficiency through Redistributing Limited Resources --- p.28
Chapter 4.1 --- A Reallocation Rule - Short to Long (S2L) --- p.29
Chapter 4.2 --- Performance Enhancement After Applying S2L --- p.33
Chapter 4.3 --- Optimized Capability Distribution in Detail --- p.36
Chapter 4.4 --- Summary --- p.44
Chapter 5 --- N-person Evolutionary Snowdrift Game: A Review --- p.47
Chapter 5.1 --- Snowdrift Game (SG) and Evolutionary Snowdrift Game (ESG) --- p.47
Chapter 5.2 --- N-person Evolutionary Snowdrift Game --- p.49
Chapter 5.2.1 --- Payoffs of C-character and D-character Agents --- p.49
Chapter 5.2.2 --- Replicator Dynamics --- p.50
Chapter 5.2.3 --- Numerical Simulations --- p.52
Chapter 5.3 --- Summary --- p.55
Chapter 6 --- NESG on Complex Network --- p.56
Chapter 6.1 --- Models --- p.57
Chapter 6.2 --- Results of Model A (varying N) --- p.58
Chapter 6.2.1 --- Correlation of Characters in Degree --- p.60
Chapter 6.2.2 --- Correlation of Characters in Distance --- p.62
Chapter 6.3 --- Results of Model B (Fixed-N) --- p.63
Chapter 6.3.1 --- Correlation of Characters in Degree --- p.64
Chapter 6.4 --- Summary --- p.69
Chapter 7 --- Synchronization: A Review --- p.71
Chapter 7.1 --- Kuramoto Model --- p.72
Chapter 7.1.1 --- Analytical Method --- p.74
Chapter 7.1.2 --- Numerical Method --- p.78
Chapter 7.1.3 --- Summary of Kuramoto Model --- p.81
Chapter 7.2 --- Integrate-and-Fire Model --- p.81
Chapter 8 --- Kuramoto Model with Spatially Distributed Oscillators --- p.84
Chapter 8.1 --- Model --- p.84
Chapter 8.2 --- Numerical Results --- p.85
Chapter 8.3 --- Analytic Results --- p.87
Chapter 8.4 --- Summary --- p.90
Chapter 9 --- Integrate-Fire-and-Run Model --- p.92
Chapter 9.1 --- Model --- p.92
Chapter 9.2 --- Two-Body System --- p.94
Chapter 9.2.1 --- Case I: Oscillators A and B are in different sites --- p.94
Chapter 9.2.2 --- Case II: Oscillators A and B are in the same site --- p.101
Chapter 9.2.3 --- Comparison of Analytic Results and Numerical Simulations of Two-Body system --- p.103
Chapter 9.3 --- N-Body System --- p.105
Chapter 9.4 --- Summary --- p.111
Chapter 10 --- Outlook --- p.112
Bibliography --- p.114
Książki na temat "Topic evolution networks"
Verloo, Nanke, i Luca Bertolini, red. Seeing the City. NL Amsterdam: Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463728942.
Pełny tekst źródłaThe Myc/Max/Mad Transcription Factor Network (Current Topics in Microbiology and Immunology). Springer, 2006.
Znajdź pełny tekst źródłaPrescott, Tony J., i Leah Krubitzer. Evo-devo. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0008.
Pełny tekst źródłaHalassa, Michael M., red. The Thalamus. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108674287.
Pełny tekst źródłaChow, Peter C. Taiwan in the Global Economy. Greenwood Publishing Group, Inc., 2002. http://dx.doi.org/10.5040/9798216022121.
Pełny tekst źródłaChild, John, David Faulkner, Stephen Tallman i Linda Hsieh. Cooperative Strategy. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198814634.001.0001.
Pełny tekst źródłaDuhaime, Irene M., Michael A. Hitt i Majorie A. Lyles, red. Strategic Management. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190090883.001.0001.
Pełny tekst źródłaMacdonald, David W., Chris Newman i Lauren A. Harrington, red. Biology and Conservation of Musteloids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.001.0001.
Pełny tekst źródłaHotson, Howard. The Reformation of Common Learning. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199553389.001.0001.
Pełny tekst źródłaBurns, Kelli S. Social Media. ABC-CLIO, LLC, 2017. http://dx.doi.org/10.5040/9798216015888.
Pełny tekst źródłaCzęści książek na temat "Topic evolution networks"
Rahimi, Hamed, Hubert Naacke, Camelia Constantin i Bernd Amann. "ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives". W Complex Networks & Their Applications XII, 332–43. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53472-0_28.
Pełny tekst źródłaLe Coze, Jean-Christophe. "Safety and Subcontracting". W SpringerBriefs in Applied Sciences and Technology, 1–14. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35163-1_1.
Pełny tekst źródłaSangiorgio, Matteo. "Deep Learning in Multi-step Forecasting of Chaotic Dynamics". W Special Topics in Information Technology, 3–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_1.
Pełny tekst źródłaTangi, Marco. "Dynamic Sediment Connectivity Modelling for Strategic River Basin Planning". W Special Topics in Information Technology, 27–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_3.
Pełny tekst źródłaStadler, Peter F. "Evolution of RNA-Based Networks". W Current Topics in Microbiology and Immunology, 43–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/82_2015_470.
Pełny tekst źródłaZhou, Qingling, Genying Wang i Haiqiang Chen. "A Topic Evolution Model Based on Microblog Network". W Lecture Notes in Electrical Engineering, 791–98. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7262-5_89.
Pełny tekst źródłaBioglio, Livio, Ruggero G. Pensa i Valentina Rho. "TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks". W Machine Learning and Knowledge Discovery in Databases, 432–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71273-4_46.
Pełny tekst źródłaZhang, Lina. "System of Cross-Border E-commerce Network Pattern Evolution on Account of Bayes-BP Algorithm". W Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence, 191–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2092-1_24.
Pełny tekst źródłaMalavena, Gerardo. "Modeling of GIDL–Assisted Erase in 3–D NAND Flash Memory Arrays and Its Employment in NOR Flash–Based Spiking Neural Networks". W Special Topics in Information Technology, 43–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_4.
Pełny tekst źródłaZheng, Yipu, Zhuqian Zhou i Paulo Blikstein. "Towards an Inclusive and Socially Committed Community in Artificial Intelligence in Education: A Social Network Analysis of the Evolution of Authorship and Research Topics over 8 Years and 2509 Papers". W Lecture Notes in Computer Science, 414–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11644-5_34.
Pełny tekst źródłaStreszczenia konferencji na temat "Topic evolution networks"
Jung, Sukhwan, i Aviv Segev. "Semantic Similarity Analysis between Future Topics and Their Neighbors in Topic Networks for Network-based Topic Evolution". W 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020287.
Pełny tekst źródłaZehnalova, S., Z. Horak, M. Kudelka i V. Snasel. "Evolution of Author's Topic in Authorship Network". W 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.208.
Pełny tekst źródłaLiang, Wei, Zixian Lu, Qun Jin, Yonghua Xiong i Min Wu. "Modeling of Research Topic Evolution Associated with Social Networks of Researchers". W 2015 IEEE 12th Intl. Conf. on Ubiquitous Intelligence and Computing, 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2015. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.213.
Pełny tekst źródłaGuo, Tinghao, Jiarui Xu, Yue Sun, Yilin Dong, Neal E. Davis i James T. Allison. "Network Analysis of Design Automation Literature". W ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67361.
Pełny tekst źródłaZhao, Yunwei, Can Wang, Chi-Hung Chi, Willem-Jan van den Heuvel, Kwok-Yan Lam i Min Shu. "Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution". W 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852290.
Pełny tekst źródłaBesharatloo, Mohammad, Atiye Rahimizadeh i Masoud Besharatloo. "Hybrid Intrusion Detection Model for Computer Networks". W 11th International Conference on Signal Image Processing and Multimedia. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130906.
Pełny tekst źródłaHakimova, Aida. "Network Approach for Visualizing the Evolution of the Research of Cross-lingual Semantic Similarity". W International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce2773d960b0.37534641.
Pełny tekst źródłaPeres, Leandro, Pablo Cecilio, Francielly Rodrigues, Nícollas Silva i Leonardo Rocha. "An overview of Brazilian researches in the Computer Science field in last years". W VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8783.
Pełny tekst źródłaNguyen, Thuc, i Phuc Do. "Discovering Topic Evolution in Heterogeneous Bibliographic Network". W 2018 10th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2018. http://dx.doi.org/10.1109/kse.2018.8573400.
Pełny tekst źródłaYe, Chunlei, Dongmei Liu, Na Chen i Li Lin. "Mapping the topic evolution using citation-topic model and social network analysis". W 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382375.
Pełny tekst źródłaRaporty organizacyjne na temat "Topic evolution networks"
Payment Systems Report - June of 2020. Banco de la República de Colombia, luty 2021. http://dx.doi.org/10.32468/rept-sist-pag.eng.2020.
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