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Статті в журналах з теми "Temporal Graph Processing"
Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Sonar target feature representation using temporal graph networks." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A102. http://dx.doi.org/10.1121/10.0010791.
Повний текст джерелаChoi, Jeongwhan, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. "Graph Neural Controlled Differential Equations for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6367–74. http://dx.doi.org/10.1609/aaai.v36i6.20587.
Повний текст джерелаZhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (April 26, 2023): 2001. http://dx.doi.org/10.3390/electronics12092001.
Повний текст джерелаLiu, Jun. "Motion Action Analysis at Basketball Sports Scene Based on Image Processing." Scientific Programming 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/7349548.
Повний текст джерелаLi, Jing, Wenyue Guo, Haiyan Liu, Xin Chen, Anzhu Yu, and Jia Li. "Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data." ISPRS International Journal of Geo-Information 10, no. 8 (August 17, 2021): 555. http://dx.doi.org/10.3390/ijgi10080555.
Повний текст джерелаKe, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (October 31, 2022): 1–30. http://dx.doi.org/10.1145/3494561.
Повний текст джерелаZhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.
Повний текст джерелаZheng, Xiaolong, Dongdong Guan, Bangjie Li, Zhengsheng Chen, and Lefei Pan. "Global and Local Graph-Based Difference Image Enhancement for Change Detection." Remote Sensing 15, no. 5 (February 21, 2023): 1194. http://dx.doi.org/10.3390/rs15051194.
Повний текст джерелаSteinbauer, Matthias, and Gabriele Anderst Kotsis. "DynamoGraph: extending the Pregel paradigm for large-scale temporal graph processing." International Journal of Grid and Utility Computing 7, no. 2 (2016): 141. http://dx.doi.org/10.1504/ijguc.2016.077491.
Повний текст джерелаChen, Yaosen, Bing Guo, Yan Shen, Wei Wang, Weichen Lu, and Xinhua Suo. "Boundary graph convolutional network for temporal action detection." Image and Vision Computing 109 (May 2021): 104144. http://dx.doi.org/10.1016/j.imavis.2021.104144.
Повний текст джерелаДисертації з теми "Temporal Graph Processing"
Kumar, Rohit 1986. "Temporal graph mining and distributed processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/620623.
Повний текст джерелаCon el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacción humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo también están en alza. Todas estas interacciones no son más que una representación de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades únicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos información y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo estático G=(V, E). Las redes de interacción se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacción sobre una arista representa una interacción entre dos usuarios (correo electrónico, llamada, retweet), o en el caso de una red financiera una interacción entre dos cuentas para representar una transacción. Analizamos las redes de interacción bajo múltiples escenarios. En el primero, estudiamos las redes de interacción bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar información a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario añadimos restricciones adicionales respecto como la información fluye entre nodos. Asumimos que un nodo puede mandar información a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales también asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximización de influencia restringido temporalmente. Analizando los datos de la red de interacción bajo nuestro modelo intentamos descubrir los k nodos más influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicación-a-ubicación usando datos de redes sociales basades en localización (LBSNs). En el mismo escenario también minamos camínos cíclicos temporales para entender los patrones de comunicación en una red. Existen múltiples aplicaciones para cíclos temporales y aparecen naturalmente en redes de comunicación donde una persona envía un mensaje y después de un tiempo reacciona a una cadena de reacciones de compañeros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros análisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, también estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas máquinas. Gran cantidad de investigación se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partición no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento actúa dado un grafo y un algoritmo.
Kumar, Rohit. "Temporal Graph Mining and Distributed Processing." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/271527.
Повний текст джерелаDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Zhang, Hui. "Temporal subtraction of chest radiograph using graph cuts and free-form deformations." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/b40203451.
Повний текст джерелаZhang, Hui, and 張暉. "Temporal subtraction of chest radiograph using graph cuts and free-form deformations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B40203451.
Повний текст джерелаBautista, Ruiz Esteban. "Laplacian Powers for Graph-Based Semi-Supervised Learning." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEN081.
Повний текст джерелаGraph-Based Semi-Supervised Learning (G-SSL) techniques learn from both labelled and unla- belled data to build better classifiers. Despite successes, its performance can still be improved, particularly in cases of graphs with unclear clusters or unbalanced labelled datasets. To ad- dress such limitations, the main contribution of this dissertation is a novel method for G-SSL referred to as the Lγ -PageRank method. It consists of a generalization of the PageRank algo- rithm based on the positive γ-th powers of the graph Laplacian matrix. The theoretical study of Lγ -PageRank shows that (i) for γ < 1, it corresponds to an extension of the PageRank algo- rithm to L´evy processes: where random walkers can now perform far-distant jumps in a single step; and (ii) for γ > 1, it operates on signed graphs: where nodes belonging to one same class are more likely to share positive edges while nodes from different classes are more likely to be connected with negative edges. We show the existence of an optimal γ-th power that maximizes performance, for which a method for its automatic estimation is devised and assessed. Exper- iments on several datasets demonstrate that the L´evy flight random walkers can enhance the detection of classes with complex local structures and that the signed graphs can significantly improve the separability of data and also override the issue of unbalanced labelled data. In addition, we study efficient implementations of Lγ -PageRank. Extensions of Power Iteration and Gauss-Southwell, successful algorithms to efficiently compute the solution of the standard PageRank algorithm, are derived for Lγ -PageRank. Moreover, the dynamic versions of Power Iteration and Gauss-Southwell, which can update the solution of standard PageRank in sub- linear complexity when the graph evolves or new data arrive, are also extended to Lγ -PageRank. Lastly, we apply Lγ -PageRank in the context of Internet routing. We address the problem of identifying the Autonomous Systems (AS) of inter-AS links from the network of IP addresses and AS public registers. Experiments on tracerout measurements collected from the Internet show that Lγ -PageRank can solve this inference task with no errors, even when the expert does not provide labelled examples of all classes
Chao, Tian-Jy. "Estimating temporary file sizes for query graphs in distributed relational database systems." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/90921.
Повний текст джерелаM.S.
Rajaei, Hoda. "Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3882.
Повний текст джерелаHamon, Ronan. "Analyse de réseaux temporels par des méthodes de traitement du signal : application au système de vélos en libre-service à Lyon." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1017/document.
Повний текст джерелаBike-sharing systems have become essential elements in urban transportation systems of many world's big cities. Thanks to the data generated by these systems, it is possible to obtain a precise characterization of urban cycling, both in terms of transportation and socio-economic aspects. Taking advantage of the recent abundance of data allowed by the current technology, the challenges lie in the development of efficient data analysis method, adapted to these systems. This PhD thesis proposes some answers to this issue, first by methodological developments and second by studying real-world data obtained from the bike-sharing system in Lyon, called Vélo'v.The Vélo'v system can be represented as a network, describing a set of relations between the stations spread over the city. This representation, used for many systems, enables the use of tools from network theory to measure the network structure and understand the underlying mechanisms. Nevertheless, taking into account the dynamic evolution of the structure requires an extension of the classical tools to the temporal case. Parallels between this problem and the field of signal processing can be done, and opens the way to the consideration of connections between the description of the dynamics of temporal networks and those of signals. This work introduces a duality between temporal networks and signals, such that the analysis of the signals using the classical tools of signal processing helps to the characterization of the structure of the corresponding network.This methodology, at the juncture between signal processing and network analysis, is first justified by the study of the Vélo'v network, by comparing different data analysis method and the representation of the system as a temporal network. Then, a method to relabel the vertices of the graph according to the topology of the network is discussed, opening up a duality between networks and signals. This duality is then extended to temporal networks: The analysis of the spectral properties of the signals are studied through a fully automated extraction method, enabling the decomposition of relevant network structure over time
Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
Повний текст джерелаBaranawal, Animesh. "Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5721.
Повний текст джерелаЧастини книг з теми "Temporal Graph Processing"
Baumstark, Alexander, Muhammad Attahir Jibril, and Kai-Uwe Sattler. "Temporal Graph Processing in Modern Memory Hierarchies." In Advances in Databases and Information Systems, 103–16. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42914-9_8.
Повний текст джерелаBattistone, Francesco, Alfredo Petrosino, and Gabriella Sanniti di Baja. "GRUNTS: Graph Representation for UNsupervised Temporal Segmentation." In Image Analysis and Processing — ICIAP 2015, 225–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_21.
Повний текст джерелаBrisaboa, Nieves R., Diego Caro, Antonio Fariña, and M. Andrea Rodríguez. "A Compressed Suffix-Array Strategy for Temporal-Graph Indexing." In String Processing and Information Retrieval, 77–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11918-2_8.
Повний текст джерелаDai, Qianwen, Fang Kong, and Qianying Dai. "Event Temporal Relation Classification Based on Graph Convolutional Networks." In Natural Language Processing and Chinese Computing, 393–403. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32236-6_35.
Повний текст джерелаChen, Ya, Wanrong Jiang, Hao Fu, and Guiquan Liu. "Spatio-Temporal Dynamic Multi-graph Attention Network for Ride-Hailing Demand Prediction." In Neural Information Processing, 133–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92270-2_12.
Повний текст джерелаWang, Huimu, Zhen Liu, Zhiqiang Pu, and Jianqiang Yi. "STGA-LSTM: A Spatial-Temporal Graph Attentional LSTM Scheme for Multi-agent Cooperation." In Neural Information Processing, 663–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63833-7_56.
Повний текст джерелаMa, Jingyuan, Zhan Shi, Shang Liu, Wang Zhang, Yutong Wu, Fang Wang, and Dan Feng. "LSM-Subgraph: Log-Structured Merge-Subgraph for Temporal Graph Processing." In Web and Big Data, 477–94. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25158-0_39.
Повний текст джерелаKang, Junjun, and Fang Kong. "DialogueTRGAT: Temporal and Relational Graph Attention Network for Emotion Recognition in Conversations." In Natural Language Processing and Chinese Computing, 460–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17120-8_36.
Повний текст джерелаOuyang, Wenli, Yahong Zhang, Mingda Zhu, Xiuling Zhang, Hongye Chen, Yinghao Ren, and Wei Fan. "Interpretable Spatial-Temporal Attention Graph Convolution Network for Service Part Hierarchical Demand Forecast." In Natural Language Processing and Chinese Computing, 575–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32236-6_52.
Повний текст джерелаWu, Huanhuan, Yunjian Zhao, James Cheng, and Da Yan. "Efficient Processing of Growing Temporal Graphs." In Database Systems for Advanced Applications, 387–403. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55699-4_24.
Повний текст джерелаТези доповідей конференцій з теми "Temporal Graph Processing"
Araghi, Hesam, Massoud Babaie-Zadeh, and Sophie Achard. "Dynamic K-Graphs: an Algorithm for Dynamic Graph Learning and Temporal Graph Signal Clustering." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287661.
Повний текст джерелаGuillou, Liane, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman. "Incorporating Temporal Information in Entailment Graph Mining." In Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.textgraphs-1.7.
Повний текст джерелаMostafa, Abdelrahman, Wei Peng, and Guoying Zhao. "Hyperbolic Spatial Temporal Graph Convolutional Networks." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897522.
Повний текст джерелаWu, Jiapeng, Meng Cao, Jackie Chi Kit Cheung, and William L. Hamilton. "TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion." In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.emnlp-main.462.
Повний текст джерелаSun, Haohai, Shangyi Geng, Jialun Zhong, Han Hu, and Kun He. "Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs." In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.emnlp-main.507.
Повний текст джерелаIsufi, Elvin, Andreas Loukas, Andrea Simonetto, and Geert Leus. "Separable autoregressive moving average graph-temporal filters." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760238.
Повний текст джерелаDal Col, Alcebiades, and Luis Gustavo Nonato. "Visual Analytics via Graph Signal Processing." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8295.
Повний текст джерелаGuo, Jin, Zhen Han, Su Zhou, Jiliang Li, Volker Tresp, and Yuyi Wang. "Continuous Temporal Graph Networks for Event-Based Graph Data." In Proceedings of the 2nd Workshop on Deep Learning on Graphs for Natural Language Processing (DLG4NLP 2022). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.dlg4nlp-1.3.
Повний текст джерелаCheng, Zida, Siheng Chen, and Ya Zhang. "Spatio-Temporal Graph Complementary Scattering Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747790.
Повний текст джерелаYao, Jiarui, Steven Bethard, Kristin Wright-Bettner, Eli Goldner, David Harris, and Guergana Savova. "Textual Entailment for Temporal Dependency Graph Parsing." In Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.25.
Повний текст джерела