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Relevant bibliographies by topics / Cascade prediction

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Academic literature on the topic 'Cascade prediction'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Cascade prediction"

1

Suzuki, Daiki, Sho Tsugawa, Keiichiro Tsukamoto, and Shintaro Igari. "On the effectiveness of a contrastive cascade graph learning framework: The power of synthetic cascade data." PLOS ONE 18, no. 10 (2023): e0293032. http://dx.doi.org/10.1371/journal.pone.0293032.

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Analyzing the dynamics of information diffusion cascades and accurately predicting their behavior holds significant importance in various applications. In this paper, we concentrate specifically on a recently introduced contrastive cascade graph learning framework, for the task of predicting cascade popularity. This framework follows a pre-training and fine-tuning paradigm to address cascade prediction tasks. In a previous study, the transferability of pre-trained models within the contrastive cascade graph learning framework was examined solely between two social media datasets. However, in o

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Huang, Ningbo, Gang Zhou, Mengli Zhang, Meng Zhang, and Ze Yu. "Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–12. http://dx.doi.org/10.1155/2021/7880215.

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Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in info

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3

Lan, Xinyue, Liyue Wang, Cong Wang, Gang Sun, Jinzhang Feng, and Miao Zhang. "Prediction of Transonic Flow over Cascades via Graph Embedding Methods on Large-Scale Point Clouds." Aerospace 10, no. 12 (2023): 1029. http://dx.doi.org/10.3390/aerospace10121029.

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In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed improvement compared to traditional CFD calculations while maintaining a commendable level of accuracy. Taking advantage of a multilayer graph structure, the framework can extract both global and local information from the cascade flow field simultaneously and present prediction over unstructured data. In line with the results obtained from the

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Sun, Ling, Yuan Rao, Xiangbo Zhang, Yuqian Lan, and Shuanghe Yu. "MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4156–64. http://dx.doi.org/10.1609/aaai.v36i4.20334.

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Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enha

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5

Abufouda, Mohammed. "Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction." Complexity 2018 (October 9, 2018): 1–17. http://dx.doi.org/10.1155/2018/3873601.

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Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inacti

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6

Cheng, Zhangtao, Xovee Xu, Ting Zhong, Fan Zhou, and Goce Trajcevski. "CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16192–93. http://dx.doi.org/10.1609/aaai.v37i13.26956.

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Predicting information cascade popularity is a fundamental problem for understanding the nature of information propagation on social media. However, existing works fail to capture an essential aspect of information propagation: the temporal irregularity of cascade event -- i.e., users' re-tweetings at random and non-periodic time instants. In this work, we present a novel framework CasODE for information cascade prediction with neural ordinary differential equations (ODEs). CasODE generalizes the discrete state transitions in RNNs to continuous-time dynamics for modeling the irregular-sampled

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7

Wang, Zhengang, Xin Wang, Fei Xiong, and Hongshu Chen. "A Survey of Deep Learning-Based Information Cascade Prediction." Symmetry 16, no. 11 (2024): 1436. http://dx.doi.org/10.3390/sym16111436.

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Online social media have significantly boosted the creation and transmission of information, accelerating the dissemination and interaction of vast amounts of data, thereby making the prediction of information cascades increasingly important. In recent years, deep learning has been extensively applied in the domain of information cascade prediction. This paper primarily classifies, organizes, and summarizes the current research status and classic algorithms of information cascade prediction methods based on deep learning. According to the different focuses on characterizing information cascade

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Li, Gang, Tao Meng, Min Li, Mingle Zhou, and Delong Han. "A Dynamic Short Cascade Diffusion Prediction Network Based on Meta-Learning-Transformer." Electronics 12, no. 4 (2023): 837. http://dx.doi.org/10.3390/electronics12040837.

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The rise of social networks has greatly contributed to creating information cascades. Over time, new nodes are added to the cascade network, which means the cascade network is dynamically variable. At the same time, there are often only a few nodes in the cascade network before new nodes join. Therefore, it becomes a key task to predict the diffusion after the dynamic cascade based on the small number of nodes observed in the previous period. However, existing methods are limited for dynamic short cascades and cannot combine temporal information with structural information well, so a new model

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9

Zhou, Bangzhu, Xiaodong Feng, and Hemin Feng. "Structural-topic aware deep neural networks for information cascade prediction." PeerJ Computer Science 10 (February 19, 2024): e1870. http://dx.doi.org/10.7717/peerj-cs.1870.

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It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascad

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Han, Jinyoung, Daejin Choi, Jungseock Joo, and Chen-Nee Chuah. "Predicting Popular and Viral Image Cascades in Pinterest." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 82–91. http://dx.doi.org/10.1609/icwsm.v11i1.14879.

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The word-of-mouth diffusion has been regarded as an important mechanism to advertise a new idea, image, technology, or product in online social networks (OSNs). This paper studies the prediction of popular and viral image diffusion in Pinterest. We first characterize an image cascade from two perspectives: (i) volume — how large the cascade is, that is, total number of users reached, and (ii) structural virality — how many users in the cascade are responsible for attracting other users. Our model predicts whether an image will be (a) popular in terms of the volume of its cascade, or (b) viral

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Dissertations / Theses on the topic "Cascade prediction"

1

Adistambha, Kevin. "Embedded lossless audio coding using linear prediction and cascade coding." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060724.122433/index.html.

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Thomas, Gregory David. "Measurement and prediction of the flow through an annular turbine cascade." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA272530.

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Yaras, Metin Ilbay Carleton University Dissertation Engineering Aeronautical. "Measurement and prediction of tip-clearance effects in a linear turbine cascade." Ottawa, 1990.

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Horne, Kyle S. "Nano-scale Thermal Property Prediction by Molecular Dynamics Simulation with Experimental Validation." DigitalCommons@USU, 2014. https://digitalcommons.usu.edu/etd/3089.

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Quantum cascade laser (QCL) diodes have potential applications in many areas including emissions analysis and explosives detection, but like many solid-state devices they suer from degraded performance at higher temperatures. To alleviate this drawback, the thermal properties of the QCL diodes must be better understood. Using molecular dynamics (MD) and photothermal radiometry (PTR), the thermal conductivity of a representative QCL diode is computed and measured respectively. The MD results demonstrate that size eects are present in the simulated systems, but if these are accounted for by norm

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Nunes, Bonaventure R. "Numerical Loss Prediction of high Pressure Steam Turbine airfoils." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/51742.

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Steam turbines are widely used in various industrial applications, primarily for power extraction. However, deviation for operating design conditions is a frequent occurrence for such machines, and therefore, understanding their performance at off design conditions is critical to ensure that the needs of the power demanding systems are met as well as ensuring safe operation of the steam turbines. In this thesis, the aerodynamic performance of three different turbine airfoil sections ( baseline, mid radius and tip profile) as a function of angle of incidence and exit Mach numbers, is numericall

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Wang, Chun-Wei. "Prediction of turbine cascade flows with a quasi-three-dimensional rotor viscous code and the extension of the algebraic turbulence model." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/24012.

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Approved for public release; distribution is unlimited<br>A quasi- three-dimensional rotor viscous code is used to predict high subsonic flow through an annular cascade of turbine blades. The well known Baldwin- Lomax turbulence model is used in the program. An attempt was made to implement a new turbulence model, based on renormalization group theory in the program. This was done to improve the prediction of the boundary layer transition on the blade surfaces . and subsequent wake development. The comparison of these two turbulence models with experimental data are presented. Pressure

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Leggett, John. "Detailed investigation of loss prediction of an axial compressor cascade at off-design conditions in the presence of incident free-stream disturbances using large eddy simulations." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/422285/.

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The prediction of an axial compressor’s loss early on in the design phase is a valuable and important part of the design process. The work presented here focuses on assessing the accuracy of current prediction methods, Reynolds Averaged Navier Stokes (RANS), compared with highly accurate Large Eddy Simulations (LES). The simulations were performed at the challenging running conditions of engine relevant Mach (0.67) and Reynolds (300,000) numbers. The work looks at the effects of off-design incidence and the influence of different free-stream disturbances on loss prediction. From the highly acc

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Elazar, Yekutiel. "A mapping of the viscous flow behavior in a controlled diffusion compressor cascade using laser doppler velocimetry and preliminary evaluation of codes for the prediction of stall." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23296.

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Detailed measurements were made at M=0.25 and Re sub c = 700000 of the flow through a linear compressor cascade of controlled diffusion (CD) blading using a two-component argon-ion laser doppler velocimeter system. The measurements included mapping of the inviscid flow in the passage between two adjacent blades, boundary layer surveys, and wake surveys. Viscous flow phenomena such as a laminar separation region with reattachment on the suction surface, and laminar to turbulent transition on the pressure surface were resolved, and the viscous growth to the trailing edge was defined for three in

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Ghosh, Sucheta. "End-to-End Discourse Parsing with Cascaded Structured Prediction." Doctoral thesis, Università degli studi di Trento, 2012. https://hdl.handle.net/11572/367657.

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Parsing discourse is a challenging natural language processing task. In this research work first we take a data driven approach to identify arguments of explicit discourse connectives. In contrast to previous work we do not make any assumptions on the span of arguments and consider parsing as a token-level sequence labeling task. We design the argument segmentation task as a cascade of decisions based on conditional random fields (CRFs). We train the CRFs on lexical, syntactic and semantic features extracted from the Penn Discourse Treebank and evaluate feature combinations on the commonly

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Ghosh, Sucheta. "End-to-End Discourse Parsing with Cascaded Structured Prediction." Doctoral thesis, University of Trento, 2012. http://eprints-phd.biblio.unitn.it/733/1/sghosh_thesis_v1.04.pdf.

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Parsing discourse is a challenging natural language processing task. In this research work first we take a data driven approach to identify arguments of explicit discourse connectives. In contrast to previous work we do not make any assumptions on the span of arguments and consider parsing as a token-level sequence labeling task. We design the argument segmentation task as a cascade of decisions based on conditional random fields (CRFs). We train the CRFs on lexical, syntactic and semantic features extracted from the Penn Discourse Treebank and evaluate feature combinations on the common

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Books on the topic "Cascade prediction"

1

Thomas, Gregory David. Measurement and prediction of the flow through an annular turbine cascade. Naval Postgraduate School, 1993.

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2

Center, NASA Glenn Research, ed. Acoustic scattering by three-dimensional stators and rotors using the SOURCE3D code. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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Wang, Chun-Wei. Prediction of turbine cascade flows with a quasi-three-dimensional rotor viscous code and the extension of the algebraic turbulence model. Naval Postgraduate School, 1992.

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Elazar, Yekutiel. A mapping of the viscous flow behavior in a controlled diffusion compressor cascade using laser doppler velocimetry and preliminary evaluation of codes for the prediction of stall. Naval Postgraduate School, 1988.

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5

Observatory, Cascades Volcano. Preparing for the next eruption in the Cascades. U.S. Geological Survey, 1994.

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Observatory, Cascades Volcano. Preparing for the next eruption in the Cascades. U.S. Dept. of the Interior, U.S. Geological Survey, Cascades Volcano Observatory, 1994.

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7

W, Giel P., and NASA Glenn Research Center, eds. Blade heat transfer measurements and predictions in a transonic turbine cascade. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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8

C, Hall Kenneth, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Development of a linearized unsteady aerodynamic analysis for cascade gust response predictions. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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9

W, Swafford Timothy, Reddy T. S. R, and Lewis Research Center, eds. Euler flow predictions for an oscillating cascade using a high resolution wave-split scheme. National Aeronautics and Space Administration, Lewis Research Center, 1991.

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R, Nelson Alan, Personius Stephen F, Rogers A. M, and Geological Survey (U.S.), eds. Earthquake hazards in the Pacific Northwest of the United States.: An overview of recent coastal geologic studies and their bearing on segmentation of Holocene ruptures, central Cascadia subduction zone. U.S. Geological Survey, 1991.

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Book chapters on the topic "Cascade prediction"

1

Tetko, Igor V., Vasyl V. Kovalishyn, Alexander I. Luik, Tamara N. Kasheva, Alessandro E. P. Villa, and David J. Livingstone. "Variable Selection in the Cascade-Correlation Learning Architecture." In Molecular Modeling and Prediction of Bioactivity. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_124.

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Li, Yangping, Xiaorui Wei, and Tianming Hu. "Cascade Spatial Autoregression for Air Pollution Prediction." In Data Mining and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_11.

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3

Liu, Chaochao, Wenjun Wang, Pengfei Jiao, Yueheng Sun, Xiaoming Li, and Xue Chen. "CPNSA: Cascade Prediction with Network Structure Attention." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67537-0_5.

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Lv, Ruilin, Chengxi Zang, Wai Kin (Victor) Chan, and Wenwu Zhu. "Analyzing WeChat Diffusion Cascade: Pattern Discovery and Prediction." In Smart Service Systems, Operations Management, and Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_34.

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Grabec, Igor. "Cascade Neural Network Developed for Time Series Prediction." In ICANN ’93. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_112.

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Bashir, Aminat T., Abdullateef O. Balogun, Matthew O. Adigun, et al. "Cascade Generalization-Based Classifiers for Software Defect Prediction." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70285-3_4.

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Tong, Chunyan, Zhanwei Xuan, Song Yang, et al. "Topic-Aware Model for Early Cascade Population Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28124-2_47.

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Chunyan, Tong, Zhang Kai, Yang Song, et al. "Context-User Dependent Model for Cascade Retweeter Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28124-2_61.

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Liu, Wei, Huawei Shen, Wentao Ouyang, Ge Fu, Li Zha, and Xueqi Cheng. "Learning Cost-Effective Social Embedding for Cascade Prediction." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2993-6_1.

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Fu, Wai-Tat, and Vera Liao. "Crowdsourcing Quality Control of Online Information: A Quality-Based Cascade Model." In Social Computing, Behavioral-Cultural Modeling and Prediction. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19656-0_23.

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Conference papers on the topic "Cascade prediction"

1

Huang, Shi-bo, Gang Zhang, Hao-bo Liu, Chen-xi Zhang, and Jian-wei Liu. "CaSTGCN: Deep Learning Method for Information Cascade Prediction." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865147.

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Zhang, Yibo, Dewei Li, Yuanqiang Zhou, and Furong Gao. "Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662413.

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Nahak, Pradeep, Jyotirmay Singh, Dilip Kumar Pratihar, and Alok Kanti Deb. "Tomato Ripening Stage Prediction Using a Cascade Model of YOLOv8 and ViT." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011385.

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Fan, Xingguo, Xudong Li, Yang Yu, Haiyu Wang, He Yan, and Kun Fang. "Prediction of Strip Finish Rolling Width Spread Based on Multi-Granularity Cascade Forest." In 2024 IEEE International Conference on Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA). IEEE, 2024. http://dx.doi.org/10.1109/aimera59657.2024.10735459.

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Zhou, Mingyang, Yanjie Lin, Gang Liu, Zuwen Li, Hao Liao, and Rui Mao. "Modeling Personalized Retweeting Behaviors for Multi-Stage Cascade Popularity Prediction." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/287.

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Predicting the size of message cascades is critical in various applications, such as online advertising and early detection of rumors. However, most existing deep learning approaches rely on cascade observation, which hinders accurate cascade prediction before message posting. Besides, these approaches overlook personalized retweeting behaviors that reflect users' inclination to retweeting specific types of information. In this study, we propose a universal cascade prediction framework, namely Cascade prediction regarding Multiple Stage (CasMS), that effectively predicts cascade popularity acr

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Lu, Xiaodong, Shuo Ji, Le Yu, Leilei Sun, Bowen Du, and Tongyu Zhu. "Continuous-Time Graph Learning for Cascade Popularity Prediction." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/247.

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Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the cascades might be correlated with each other due to the shared users or similar topics. Moreover, the preferences of users and semantics of a cascade are usually continuously evolving over time. In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal

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Wang, Yongqing, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. "Cascade Dynamics Modeling with Attention-based Recurrent Neural Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/416.

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An ability of modeling and predicting the cascades of resharing is crucial to understanding information propagation and to launching campaign of viral marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture the memory

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Li, Huacheng, Chunhe Xia, Tianbo Wang, and Haopeng Zhao. "CRCS: Learning Synergistic Cascade Correlation for Microscopic Cascade Prediction." In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta). IEEE, 2022. http://dx.doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00165.

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Agnew, Gary, Andrew Grier, Thomas Taimre, et al. "Terahertz quantum cascade laser bandwidth prediction." In 2015 International Topical Meeting on Microwave Photonics (MWP). IEEE, 2015. http://dx.doi.org/10.1109/mwp.2015.7356671.

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Guo, Ruocheng, Elham Shaabani, Abhinav Bhatnagar, and Paulo Shakarian. "Toward Order-of-Magnitude Cascade Prediction." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2809358.

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