Relevant bibliographies by topics / Network diffusion models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Network diffusion models'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Network diffusion models"

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Luu, Duc, Ee-Peng Lim, Tuan-Anh Hoang, and Freddy Chua. "Modeling Diffusion in Social Networks Using Network Properties." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 218–25. http://dx.doi.org/10.1609/icwsm.v6i1.14259.

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Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. In this paper, we establish a general probabilistic framework, which can be used to derive macro-level diffusion models, including the well known Bass Model (BM). Using this framework, we de
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Finn, Tobias Sebastian, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand. "Representation learning with unconditional denoising diffusion models for dynamical systems." Nonlinear Processes in Geophysics 31, no. 3 (2024): 409–31. http://dx.doi.org/10.5194/npg-31-409-2024.

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Abstract. We propose denoising diffusion models for data-driven representation learning of dynamical systems. In this type of generative deep learning, a neural network is trained to denoise and reverse a diffusion process, where Gaussian noise is added to states from the attractor of a dynamical system. Iteratively applied, the neural network can then map samples from isotropic Gaussian noise to the state distribution. We showcase the potential of such neural networks in proof-of-concept experiments with the Lorenz 1963 system. Trained for state generation, the neural network can produce samp
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Smith, Jeffrey A., and Jessica Burow. "Using Ego Network Data to Inform Agent-based Models of Diffusion." Sociological Methods & Research 49, no. 4 (2018): 1018–63. http://dx.doi.org/10.1177/0049124118769100.

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Agent-based modeling holds great potential as an analytical tool. Agent-based models (ABMs) are, however, also vulnerable to critique, as they often employ stylized social worlds, with little connection to the actual environment in question. Given these concerns, there has been a recent call to more fully incorporate empirical data into ABMs. This article falls in this tradition, exploring the benefits of using sampled ego network data in ABMs of cultural diffusion. Thus, instead of relying on full network data, which can be difficult and costly to collect, or no empirical network data, which
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Iacobucci, Dawn, and Thomas W. Valente. "Network Models of the Diffusion of Innovations." Journal of Marketing 60, no. 3 (1996): 134. http://dx.doi.org/10.2307/1251848.

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Adiga, A., C. J. Kuhlman, H. S. Mortveit, and A. K. S. Vullikanti. "Sensitivity of Diffusion Dynamics to Network Uncertainty." Journal of Artificial Intelligence Research 51 (September 30, 2014): 207–26. http://dx.doi.org/10.1613/jair.4330.

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Simple diffusion processes on networks have been used to model, analyze and predict diverse phenomena such as spread of diseases, information and memes. More often than not, the underlying network data is noisy and sampled. This prompts the following natural question: how sensitive are the diffusion dynamics and subsequent conclusions to uncertainty in the network structure? In this paper, we consider two popular diffusion models: Independent cascade (IC) model and Linear threshold (LT) model. We study how the expected number of vertices that are influenced/infected, for particular initial con
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Adiga, Abhijin, Chris Kuhlman, Henning Mortveit, and Anil Kumar Vullikanti. "Sensitivity of Diffusion Dynamics to Network Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 2–8. http://dx.doi.org/10.1609/aaai.v27i1.8686.

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Simple diffusion processes on networks have been used to model, analyze and predict diverse phenomena such as spread of diseases, information and memes. More often than not, the underlying network data is noisy and sampled. This prompts the following natural question: how sensitive are the diffusion dynamics and subsequent conclusions to uncertainty in the network structure? In this paper, we consider two popular diffusion models: Independent cascades (IC) model and Linear threshold (LT) model. We study how the expected number of vertices that are influenced/infected, given some initial condit
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Fujii, Nobutada, Toshiya Kaihara, and Takashi Eda. "Agent-Based Simulation of Product Diffusion with Network Externality in a Heterogeneous Consumer Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 2 (2011): 173–79. http://dx.doi.org/10.20965/jaciii.2011.p0173.

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In a product market with network externality, outperforming products are not always disseminated. Product markets are often modeled and examined by simulation to clarify product diffusion processes. In earlier work, where multiagent systems and complex networks were used to model the product market, consumer heterogeneity was not considered despite its influence on the product diffusion process. This paper introduces threshold models into multiagent-systembased simulation in complex networks to realize consumer heterogeneity. The feasibility of the proposed method is discussed using computer s
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Cheng, Shin-Ming, Vasileios Karyotis, Pin-Yu Chen, Kwang-Cheng Chen, and Symeon Papavassiliou. "Diffusion Models for Information Dissemination Dynamics in Wireless Complex Communication Networks." Journal of Complex Systems 2013 (September 4, 2013): 1–13. http://dx.doi.org/10.1155/2013/972352.

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Information dissemination has become one of the most important services of communication networks. Modeling the diffusion of information through such networks is crucial for our modern information societies. In this work, novel models, segregating between useful and malicious types of information, are introduced, in order to better study Information Dissemination Dynamics (IDD) in wireless complex communication networks, and eventually allow taking into account special network features in IDD. According to the proposed models, and inspired from epidemiology, we investigate the IDD in various c
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Shahzamal, Md, Raja Jurdak, Bernard Mans, and Frank de Hoog. "Indirect interactions influence contact network structure and diffusion dynamics." Royal Society Open Science 6, no. 8 (2019): 190845. http://dx.doi.org/10.1098/rsos.190845.

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Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT)-based diffusion that considers t
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Bakenova, Kamila, Oleksandr Kuznetsov, Iryna Artyshchuk, Aigul Shaikhanova, Ruslan Shevchuk, and Oleksandra Orobchuk. "Information Diffusion Modeling in Social Networks: A Comparative Analysis of Delay Mechanisms Using Population Dynamics." Applied Sciences 15, no. 11 (2025): 6092. https://doi.org/10.3390/app15116092.

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This study presents a comprehensive analysis of information diffusion in social networks with time delay mechanisms. We first analyze real Reddit thread data, identifying limitations in the sample size. To overcome this, we develop synthetic network models with varied structural properties. Our approach tests three delay types (constant, uniform, exponential) across different network structures, using machine learning models to identify key factors influencing information coverage. The results show that spread probability consistently impacts diffusion across all datasets. Gradient Boosting mo
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Dissertations / Theses on the topic "Network diffusion models"

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Rhomberg, Patrick. "On the parallelization of network diffusion models." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5831.

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In this thesis, we investigate methods by which discrete event network diffusion simulators may execute without the restriction of lockstep or near lockstep synchronicity. We develop a discrete event simulator that allows free clock drift between threads, develop a differential equations model to approximate communication cost of such a simulator, and propose an algorithm by which we leverage information gathered in the natural course of simulation to redistribute agents to parallel threads such that the burden of communication is lowered during future replicates.
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Dailey, Natalie S., and Natalie S. Dailey. "Neurological Models of Dyslexia." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/622935.

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The reading network is only partially understood and even less is known regarding how the network functions when reading is impaired. Dyslexia is characterized by poor phonological processing and affects roughly 5-12% of the population. The Dorsal-Ventral and Cerebellar-Deficit models propose distinct behavioral and structural differences in young adults with dyslexia. Behavioral assessments were used to determine if deficits for young adults with dyslexia were restricted to the literacy domain or dispersed among reading and associated behavioral domains. Diffusion tensor imaging (DTI) was use
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Pyo, Tae-Hyung. "Three essays on social networks and the diffusion of innovation models." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1383.

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The Bass model has been used extensively and globally to forecast the first purchases of new products. It has been named by INFORMS as one of the top 10 most influential papers published in the 50-year history of Management Science. Most models for the diffusion of innovation are deeply rooted in the work of Bass (1969). His work provides a framework to model the underlying process of innovation adaption among first-time customers. Potential customers may be connected to one another in some sort of network. Prior research has shown that the structure of a network affects adoption patterns (Dov
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Pick, Dorothy. "A RELATIONAL DIFFUSION NETWORK STUDY OF SYNCHRONOUS AND ASYNCHRONOUS INTERNET-BASED FACULTY'S PERSONAL NETWORK EXPOSURE MODELS R." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2409.

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For many faculty, teaching online represents a new instructional delivery method, requiring the development of new teaching skills. This exploratory investigation builds upon Rogers' (2003) Diffusion of Innovations theory and communication channels to describe the influence of faculty discussions on their perceptions and decisions about teaching and learning. A sequential explanatory mixed-methods research design, using both sociometric and phenomenological methodologies, guided the exploration of faculty personal network exposure models and social learning opportunities. The study utilized on
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Trevelyan, Alexander. "Nonequilibrium Statistical Models: Guided Network Growth Under Localized Information and Perspectives on Electron Diffusion in Conductors." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23915.

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The ability to probe many-particle systems on a microscopic level has revolutionized the way we do statistical physics. As computational capabilities continue to grow exponentially, larger and more complex systems come within reach of microscopic analysis. In the field of network growth, the classical model has given way to competitive processes, in which networks are guided by some criteria at every step of their formation. We develop and analyze a new competitive growth process that permits intervention on growing networks using only local properties of the network when evaluating how
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Nath, Madhurima. "Application of Network Reliability to Analyze Diffusive Processes on Graph Dynamical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/86841.

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Moore and Shannon's reliability polynomial can be used as a global statistic to explore the behavior of diffusive processes on a graph dynamical system representing a finite sized interacting system. It depends on both the network topology and the dynamics of the process and gives the probability that the system has a particular desired property. Due to the complexity involved in evaluating the exact network reliability, the problem has been classified as a NP-hard problem. The estimation of the reliability polynomials for large graphs is feasible using Monte Carlo simulations. However, the nu
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CATTANI, ANNA. ""Multispecies" models to describe large neuronal networks." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2535730.

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Today modeling large neural network is a topic more relevant than ever. In particular, the set up of computer simulations describing complex networks with a huge number of nodes is a formidable challenge. The intrinsic difficulties concerning the prohibitive computational costs may be handled to some extent by exploiting what we call ``multispecies'' models. From a mathematical perspective this issue consists in formalizing the PDE-based continuum models which describe the high-density populations inside the network and studying interactions between them and the ODE-based discrete models for
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RUISI, Francesca. "Social network analysis and political phenomena: a contribution to the small world theory." Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/91241.

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Martinet, Lucie. "Réseaux dynamiques de terrain : caractérisation et propriétés de diffusion en milieu hospitalier." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1010/document.

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Durant cette thèse, nous nous sommes intéressés aux outils permettant d'extraire les propriétés structurelles et temporelles de réseaux dynamiques ainsi que les caractéristiques de certains scénarios de diffusion pouvant s'opérer sur ces réseaux. Nous avons travaillé sur un jeu de données spécifiques, issu du projet MOSAR, qui comporte entre autre le réseau de proximité des personnes au cours du temps durant 6 mois à l'hôpital de Berk-sur-mer. Ce réseau est particulier dans le sens où il est constitué de trois dimensions: temporelle, structurelle par la répartition des personnes en services et
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Carreira, Ferreira Sonia. "Modélisation du transport intragranulaire dans un réacteur catalytique." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1002/document.

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L'activité chimique des catalyseurs a longuement été le coeur des travaux R&D, conduisant à une influence accrue des limitations diffusionnelles internes. Il est donc important de quantifier et modéliser ces limitations dans le but d'optimiser la conception et les performances des catalyseurs.Dans le cadre de notre projet, en s'appuyant sur une approche de Monte Carlo, des réseaux aléatoires en 2D ou 3D, constitués par des pores cylindriques interconnectés, sont générés de façon à reproduire la porosité, la surface spécifique et le volume poreux des supports d'alumine-gamma. Cet outil est
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Books on the topic "Network diffusion models"

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Pacut, Andrzej. Stochastic modeling at diverse scales: From Poisson to network neurons. Oficyna Wydawnicza Politechniki Warszawskiej, 2000.

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Cotticelli, Francesco, Renzo Guardenti, and Piermario Vescovo, eds. DISME - Dissemination of Italian Spectacle Model in Europe. Firenze University Press, 2024. https://doi.org/10.36253/979-12-215-0591-7.

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DISME - Dissemination of the Italian Spectacle Model in Europe is a georeferenced digital archive dedicated to the dissemination and codification processes of Italian performance in Europe during the 18th century. Through original sources, it traces the journeys of theatre architects, stage painters, engineers, impresarios, actors, singers, musicians, dancers, playwrights, and others, demonstrating how the diffusion and adaptation of the Italian spectacle model, along with the migration of its workers, contributed to the creation of a shared language and a vast network of exchanges and relatio
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Adam, M. Shiham. Use of neural networks with advection-diffusion-reaction models to estimate large-scale movements of Skipjack tuna from tagging data. Pelagic Fisheries Research Program, University of Hawaii at Manoa, 2004.

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Blanuca, Viktor, Leonid Bezrukov, Egor Sherin, and Anatoliy Yakobson. Public geography: Digital priorities of the XXI century. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863096.

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Social geography, aimed at understanding the territorial organization of society, has a number of digital priorities that have not been analyzed before. They are related to the objects and methods of research, as well as to the methods of visualization and the areas of application of the results obtained.
 The monograph analyzes the world experience of socio-geographical study of platform economy, telecommunication networks and "smart cities", the use of data mining, models of spatial diffusion of innovations and zoning, visualization through cartographic image, dendrogram and tag cloud,
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Network models of the diffusion of innovations. Hampton Press, 1995.

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Beck, Donald Martin. The impact of social network variables on the diffusion of no-till agriculture. 1991.

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Fagiolo, Giorgio. The International Trade Network. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.29.

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This chapter critically reviews the stream of literature that has recently applied the theory of complex networks to the study of international trade. It surveys three interrelated lines of research. First it discusses descriptive work aimed at characterizing the topological properties of the international trade network (ITN) and their evolution. Second, it describes simple models that can replicate and explain those empirical properties. Third, it looks at how the topological properties of the ITN can be used as predictors of the future performance of world countries and the diffusion of econ
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Malware Diffusion Models for Wireless Complex Networks. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-02168-5.

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Karyotis, Vasileios, and M. H. R. Khouzani. Malware Diffusion Models for Modern Complex Networks: Theory and Applications. Elsevier Science & Technology Books, 2016.

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Karyotis, Vasileios, and M. H. R. Khouzani. Malware Diffusion Models for Modern Complex Networks: Theory and Applications. Elsevier Science & Technology, 2016.

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Book chapters on the topic "Network diffusion models"

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Wang, Liaoruo, Stefano Ermon, and John E. Hopcroft. "Feature-Enhanced Probabilistic Models for Diffusion Network Inference." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33486-3_32.

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Junapudi, Vasavi, Gauri K. Udgata, and Siba K. Udgata. "Study of Diffusion Models in an Academic Social Network." In Distributed Computing and Internet Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11659-9_29.

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Mossig, Ivo, Michael Windzio, Fabian Besche-Truthe, and Helen Seitzer. "Networks of Global Social Policy Diffusion: The Effects of Culture, Economy, Colonial Legacies, and Geographic Proximity." In Networks and Geographies of Global Social Policy Diffusion. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83403-6_1.

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AbstractThe introductory chapter to the volume by Mossig, Windzio, Seitzer and Besche-Truthe defines the core concepts, such as diffusion and contagion, and gives an example of an application diffusion and contagion in epidemiology. The most important underlying functions, namely the logistic density and cumulative logistic density function, are explained, followed by a very brief introduction to the core concepts of event history analysis. In the network diffusion model, contagion, or, in other words, the adoption of information or innovation, is based on the concept of exposure which will be elaborated in this chapter. Finally, after describing and visualizing the four different networks and their correlations, exponential random graph models are used to analyze structural and substantive properties of these networks. The introduction concludes with a brief overview of the chapters.
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Monreale, Anna, Francesca Naretto, and Simone Rizzo. "Agnostic Label-Only Membership Inference Attack." In Network and System Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39828-5_14.

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AbstractIn recent years we are witnessing the diffusion of AI systems based on powerful Machine Learning models which find application in many critical contexts such as medicine and financial market. In such contexts, it is important to design Trustworthy AI systems while guaranteeing privacy protection. However, some attacks on the privacy of Machine Learning models have been designed to show the threats of exposing such models. Membership Inference is one of the simplest privacy threats faced by Machine Learning models. It is based on the assumption that an adversary, observing the confidence of the model prediction, can infer whether a particular record was used for training the classifier. A variant, called Label-Only attack, exploits the adversary’s knowledge of the training data statistics to infer the record membership without accessing the confidence score of the prediction. In this paper, we propose a variant of the Label-Only attack, called Aloa, which estimates the prediction confidence exploiting a mechanism that is completely agnostic to the input data distributions. In fact, it requires neither statistical knowledge of the data nor the type of variables. Experimental results show better performance of our attack with respect to the competitors.
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Ozaki, Tomonobu, and Minoru Etoh. "Social Network Inference of Smartphone Users Based on Information Diffusion Models." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25856-5_23.

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Periquito, João S., Martin Meier, Thoralf Niendorf, Andreas Pohlmann, and Neil Peter Jerome. "Renal MRI Diffusion: Experimental Protocol." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_24.

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AbstractRenal diffusion-weighted imaging (DWI) can be used to obtain information on the microstructure of kidney tissue, and has the potential to provide MR-biomarkers for functional renal imaging. Here we describe in a step-by-step experimental protocol the MRI method for measuring renal diffusion coefficients in rodents using ADC or IVIM models. Both methods provide quantification of renal diffusion coefficients; however, IVIM, a more complex model, allows for the calculation of the pseudodiffusion and fraction introduced by tissue vascular and tubular components. DWI provides information of renal microstructure contributing to the understanding of the physiology and the underlying processes that precede the beginning of pathologies.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.
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Jerome, Neil Peter, and João S. Periquito. "Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_37.

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AbstractAnalysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.
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Breznau, Nate, and Felix Lanver. "The Global Diffusion of Work-Injury Insurance: The Role of Spatial Networks and Nation Building." In Networks and Geographies of Global Social Policy Diffusion. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83403-6_2.

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AbstractWork-injury law often marks the beginning of the modern welfare state and as we argue this type of insurance is particularly important to nation-state building: it placates myriad social groups needs and demands while binding them symbiotically with the state. We analyze any first laws and the first instance of social insurance as outcomes in 151 states (1880–2010). Our network diffusion and event history models reveal that spatial proximity and democratization are key predictors of first laws. However, nation-state formation and trade density are additional predictors of social insurance in particular. We conclude with implications of these findings for understanding nation-building within global networks.
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Saito, Kazumi, Masahiro Kimura, Kouzou Ohara, and Hiroshi Motoda. "Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network." In Advances in Social Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12079-4_20.

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Jerome, Neil Peter, Anna Caroli, and Alexandra Ljimani. "Renal Diffusion-Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concepts." In Methods in Molecular Biology. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_11.

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AbstractThe specialized function of the kidney is reflected in its unique structure, characterized by juxtaposition of disorganized and ordered elements, including renal glomerula, capillaries, and tubules. The key role of the kidney in blood filtration, and changes in filtration rate and blood flow associated with pathological conditions, make it possible to investigate kidney function using the motion of water molecules in renal tissue. Diffusion-weighted imaging (DWI) is a versatile modality that sensitizes observable signal to water motion, and can inform on the complexity of the tissue microstructure. Several DWI acquisition strategies are available, as are different analysis strategies, and models that attempt to capture not only simple diffusion effects, but also perfusion, compartmentalization, and anisotropy. This chapter introduces the basic concepts of DWI alongside common acquisition schemes and models, and gives an overview of specific DWI applications for animal models of renal disease.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.
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Conference papers on the topic "Network diffusion models"

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Mellone, Gennaro, Ciro Giuseppe De Vita, Emanuel Di Nardo, et al. "G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration." In 2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2025. https://doi.org/10.1109/pdp66500.2025.00081.

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Ajith, Meenu, and Vince D. Calhoun. "Denoising Diffusion Probabilistic Models for High-Fidelity fMRI Intrinsic Connectivity Network Data Generation." In 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2024. https://doi.org/10.1109/bhi62660.2024.10913576.

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Camerota, Chiara, Lorenzo Pappone, Tommaso Pecorella, and Flavio Esposito. "Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detection." In 2024 20th International Conference on Network and Service Management (CNSM). IEEE, 2024. https://doi.org/10.23919/cnsm62983.2024.10814607.

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Pan, Chengyi, Xiuliang Xi, Xin Jin, Huangqimei Zheng, Puming Wang, and Qiang Jiang. "Dif-GAN: A Generative Adversarial Network with Multi-Scale Attention and Diffusion Models for Infrared-Visible Image Fusion." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00267.

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Song, Zeyuan, and Zheyu Jiang. "A Physics-based, Data-driven Numerical Framework for Anomalous Diffusion of Water in Soil." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.163304.

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Precision modeling and forecasting of soil moisture are essential for implementing smart irrigation systems and mitigating agricultural drought. Most agro-hydrological models are based on the standard Richards equation, a highly nonlinear, degenerate elliptic-parabolic partial differential equation (PDE) with first order time derivative. However, research has shown that standard Richards equation is unable to model preferential flow in soil with fractal structure. In such a scenario, the soil exhibits anomalous non-Boltzmann scaling behavior. Incorporating the anomalous non-Boltzmann scaling b
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Liu, Mingxuan, Jie Gan, Rui Wen, Tao Li, Yongli Chen, and Hong Chen. "Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks." In 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). IEEE, 2024. https://doi.org/10.1109/iccbd-ai65562.2024.00110.

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Nabeel, Mohamad, Doumitrou Daniil Nimara, and Tahar Zanouda. "Fault Detection in Mobile Networks Using Diffusion Models." In 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2024. http://dx.doi.org/10.1109/iccworkshops59551.2024.10615951.

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Chen, Kang, and Yuanjie Liu. "Efficient Defocus Deblurring Networks based on Diffusion Models." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889855.

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Xie, Kangyang, Jiaan Liu, Muzhi Zhu, et al. "Unveiling Universal Forensics of Diffusion Models with Adversarial Perturbations." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650754.

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Ye, Na, and Yuan Sun. "CDM: Text-Driven Image Editing with Composable Diffusion Models." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649901.

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Reports on the topic "Network diffusion models"

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Griffiths, S. K., R. H. Nilson, and R. W. Bradshaw. In situ bioremediation: A network model of diffusion and flow in granular porous media. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/485961.

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Comola, Margherita, Rokhaya Dieye, and Bernard Fortin. Heterogeneous peer effects and gender-based interventions for teenage obesity. CIRANO, 2022. http://dx.doi.org/10.54932/tqag9043.

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This paper explores the role of gender heterogeneity in the social diffusion of obesity among adolescents and its policy implications. We propose a generalized linear social interaction model which allows for gender-dependent heterogeneity in peer effects through the channel of social synergy. We estimate the model using data on adolescent Body Mass Index and network-based interactions. Our results show that peer effects are gender-dependent, and male students are particularly responsive to the weight of their female friends. Our simulations indicate that female-tailored interventions are like
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Yilmaz, Ihsan, Ali Mamouri, Shahram Akbarzadeh, and Muhammad Omer. Authoritarian Diffusion in the Cyberspace: How Egypt Learns, Emulates, and Cooperates in Digital Authoritarianism. European Center for Populism Studies (ECPS), 2025. https://doi.org/10.55271/rp0097.

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Egypt has emerged as a key adopter and regional diffuser of digital authoritarian practices. Once limited by weak digital infrastructure, the Sisi regime has transformed the country into a technologically repressive state through sweeping legal reforms, censorship mechanisms, and expansive surveillance networks. Drawing heavily from the models of China and Russia—particularly in urban monitoring and information control—Egypt actively emulates their approaches. Crucially, both Chinese and Western technology firms have facilitated this transformation, revealing a broader pattern of global compli
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Kirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7699861.bard.

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In this project, we studied the photosynthetic apparatus during dehydration and rehydration of the homoiochlorophyllous resurrection plant Craterostigmapumilum (retains most of the photosynthetic components during desiccation). Resurrection plants have the remarkable capability to withstand desiccation, being able to revive after prolonged severe water deficit in a few days upon rehydration. Homoiochlorophyllous resurrection plants are very efficient in protecting the photosynthetic machinery against damage by reactive oxygen production under drought. The main purpose of this BARD project was
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London, Jonathan D. Adoption, Adaption, and the Iterative Challenges of Scaling up in Vietnam: Policy Entrepreneurship and System Coherence in a Major Pedagogical Reform. Research on Improving Systems of Education (RISE), 2023. http://dx.doi.org/10.35489/bsg-rise-misc_2023/11.

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Đặng Tự Ân played a pivotal role in the genesis, adoption, and diffusion of pedagogical and curricular reforms that are transforming teaching and learning in the Socialist Republic of Vietnam. His is a fascinating story of a career that began with the paralyzing disappointment of being assigned to study in a seemingly lowly teacher training college only to culminate, decades later, in his central role in the research, design, piloting, and scaling up of a reform that, despite numerous difficulties, would shape the most far-reaching and progressive curricular reforms in Vietnam’s long education
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