Network-based diffusion analysis (NBDA) is a statistical tool to detect and quantify social transmission of information or a behaviour in social networks...

Dynamic network analysis Friendship paradox Individual mobility Influence-for-hire Mathematical sociology Metcalfe's law Netocracy Network-based diffusion analysis...

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...

feeding has also been observed in solitary humpbacks. Using network-based diffusion analysis, one study argued that whales learned lobtailing from other...

recent diffusion research, even as the field has expanded into, and been influenced by, other methodological disciplines such as social network analysis and...

Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology...

and in network and dynamic analysis." The theory uses a case study of the growth of business computing to explain different mechanisms of diffusion. Diffusion...

Social network analysis (SNA) software is software which facilitates quantitative or qualitative analysis of social networks, by describing features of...

sociology. Amongst many other applications, social network analysis has been used to understand the diffusion of innovations, news and rumors. Similarly, it...

convolutional neural network that was developed for image segmentation. The network is based on a fully convolutional neural network whose architecture...

representation learning are based on diffusion maps and their extensions to handle multiple modalities. Multi-view diffusion maps address the challenge...

In cryptography, confusion and diffusion are two properties of a secure cipher identified by Claude Shannon in his 1945 classified report A Mathematical...

approaches: using (cyclic) networks with external or internal memory (model-based) learning effective distance metrics (metrics-based) explicitly optimizing...

dimensionality reduction methods such as principal component analysis (PCA), diffusion maps are part of the family of nonlinear dimensionality reduction...

Segmentation: This method is based on the idea of evolution of segmentation function which is governed by an advection-diffusion model. To segment an object...

Main path analysis is a mathematical tool, first proposed by Hummon and Doreian in 1989, to identify the major paths in a citation network, which is one...

network analysis to identify local and global patterns, locate influential entities, and examine dynamics of networks. For instance, social network analysis...

Diffusion-weighted magnetic resonance imaging (DWI or DW-MRI) is the use of specific MRI sequences as well as software that generates images from the...

Evidence-based policy (also known as evidence-informed policy or evidence-based governance) is a concept in public policy that advocates for policy decisions...

"You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis". Network and Distributed Systems Security Symposium. doi:10.14722/ndss.2020...

potentially involving ICA-based analysis, have been used in real-world cyberespionage cases. In 2010, the FBI uncovered a Russian spy network known as the "Illegals...

sociology. Amongst many other applications, social network analysis has been used to understand the diffusion of innovation, news and rumors. Similarly, it...

learning. The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell how "realistic" the...

The network is detectable through independent component analysis of resting state fMRI images, as well as seed based functional connectivity analysis. The...

recognition-inference architecture of neural networks. Artificial neural network architectures are based on inputs multiplied by weights to obtain outputs...

updated at each time step based on the current input and the previous hidden state. This feedback mechanism allows the network to learn from past inputs...

difference between diffusion maps and principal component analysis is that only local features of the data are considered in diffusion maps as opposed to...

reactions more closely, such as diffusion. One such diffusion model could conceivably consist of a transition function based on the average values of the...

including text, images and audio. Convolution-based networks are the de-facto standard in deep learning-based approaches to computer vision and image processing...

through social networks. These networks are currently being analyzed through computational methods such as network-based diffusion analysis (NBDA). In wild...