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pro vyhledávání: '"O’Sullivan, David"'
The dynamics of information diffusion in complex networks is widely studied in an attempt to understand how individuals communicate and how information travels and reaches individuals through interactions. However, complex networks often present comm
Externí odkaz:
http://arxiv.org/abs/2408.04456
Out-of-distribution data and anomalous inputs are vulnerabilities of machine learning systems today, often causing systems to make incorrect predictions. The diverse range of data on which these models are used makes detecting atypical inputs a diffi
Externí odkaz:
http://arxiv.org/abs/2403.15497
The analysis of social networks enables the understanding of social interactions, polarisation of ideas, and the spread of information and therefore plays an important role in society. We use Twitter data - as it is a popular venue for the expression
Externí odkaz:
http://arxiv.org/abs/2311.09196
Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here, we show that this strategy is particularly effective in the presence of s
Externí odkaz:
http://arxiv.org/abs/2304.10405
Understanding cascading processes on complex network topologies is paramount for modelling how diseases, information, fake news and other media spread. In this paper, we extend the multi-type branching process method developed in Keating et al., 2022
Externí odkaz:
http://arxiv.org/abs/2212.00584
Autor:
Kim, Hyungsook1,2 (AUTHOR), O'Sullivan, David Michael3 (AUTHOR) davidos@snu.ac.kr
Publikováno v:
BMC Public Health. 9/27/2024, Vol. 24 Issue 1, p1-15. 15p.
Publikováno v:
In Digital Geography and Society December 2024 7