Enterprise social network Workplaces thrive upon great communication and collaboration. Whether your company is fully remote or has its entire workforce in a brick-and-mortar office, you need your teams and employees to stay connected in order to work together effectively. That much is clear, bu...
Also here, despite the theoretical nuance differences, these two problems are explained in the same place. The irony is widely encountered a linguistic phenomenon in philosophy and linguistics, and it has been defined in various ways in the literature. Regarding this subject, various theories ...
Social network analysis 101: centrality measures explainedby Andrew Disney, 2nd January 2020Centrality measures are a vital tool for understanding networks, often also known as graphs. These algorithms use graph theory to calculate the importance of any given node in a network. They cut through ...
justified by humans2. This ‘explainability’ phenomenon limits the usage of ML models in critical real-world applications (e.g., law or traffic management) since the context of a decision is hard to be justified and explained to the end-users. Our proposed social network analysis-based visual...
First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. ...
Two of them are the mean distance (average tree depth) and diameter (maximum tree depth), which are explained in the methods section. The other two are the number of human nodes and the number of links. The first is the number of confirmed patients that make up the infection network at...
Social Network Analysis and Visualization software application. - GitHub - socnetv/app: Social Network Analysis and Visualization software application.
network. They quantify the external influences over time and describe how these influences affect the information adoption. As an outcome they found that the information tends to “jump” across the network, which can only be explained as an effect of an unobservable external influence on the ...
it can explain the variance of a known correlate of the latent variable, even when controlling for the effects of other relevant predictors. If a new scale can account for a significant portion of variance of a major correlate beyond that explained by existing scales, such a result justifies ...
Network structure can also be explained or predicted, for instance, using the p2 model (van Duijn et al., 2004) or exponential random graph models (Robins et al., 2007a, Robins et al., 2007b). Rather than focusing only on network structure, researchers collecting network data often pose ...