Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Safdari, Hadiseh"'
Autor:
Safdari, Hadiseh, De Bacco, Caterina
Anomaly detection is an essential task in the analysis of dynamic networks, as it can provide early warning of potential threats or abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community
Externí odkaz:
http://arxiv.org/abs/2404.10468
Publikováno v:
Phys. Rev. Research 5, 033084, 2023
Anomaly detection algorithms are a valuable tool in network science for identifying unusual patterns in a network. These algorithms have numerous practical applications, including detecting fraud, identifying network security threats, and uncovering
Externí odkaz:
http://arxiv.org/abs/2302.00504
Autor:
Safdari, Hadiseh, De Bacco, Caterina
Publikováno v:
Journal of Big Data 9.1 (2022): 1-20
Anomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining
Externí odkaz:
http://arxiv.org/abs/2205.06012
Autor:
De Bacco, Caterina, Contisciani, Martina, Cardoso-Silva, Jonathan, Safdari, Hadiseh, Baptista, Diego, Borges, Gabriela L., Sweet, Tracy, Young, Jean-Gabriel, Koster, Jeremy, Ross, Cody T., McElreath, Richard, Redhead, Daniel, Power, Eleanor A.
Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. There may be particular risks with multiply-reported data if people's res
Externí odkaz:
http://arxiv.org/abs/2112.11396
Publikováno v:
Journal of Complex Networks 10, cnac034 (2022)
To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structural features of the network. Therefore, attempts are always made to de
Externí odkaz:
http://arxiv.org/abs/2112.10436
Publikováno v:
Journal of Physics: Complexity 3.1 (2022): 015010
Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dyn
Externí odkaz:
http://arxiv.org/abs/2112.09624
Publikováno v:
Phys. Rev. Research 3, 023209 (2021)
We present a probabilistic generative model and efficient algorithm to model reciprocity in directed networks. Unlike other methods that address this problem such as exponential random graphs, it assigns latent variables as community memberships to n
Externí odkaz:
http://arxiv.org/abs/2012.08215
Autor:
Safdari, Hadiseh, Kamali, Milad Zare, Shirazi, Amirhossein, Khalighi, Moein, Jafari, Gholamreza, Ausloos, Marcel
Publikováno v:
PLoS ONE 11(5): e0154983 (2016)
In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we exami
Externí odkaz:
http://arxiv.org/abs/1709.02960
Publikováno v:
In Journal of Theoretical Biology September 2021
Autor:
Safdari, Hadiseh, Cherstvy, Andrey G., Chechkin, Aleksei V., Thiel, Felix, Sokolov, Igor M., Metzler, Ralf
We examine the non-ergodic properties of scaled Brownian motion, a non-stationary stochastic process with a time dependent diffusivity of the form $D(t)\simeq t^{\alpha-1}$. We compute the ergodicity breaking parameter EB in the entire range of scali
Externí odkaz:
http://arxiv.org/abs/1507.02450