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Anomalous diffusion on networks: theoretical aspects and an application to machine learning

Sarah De Nigris

Anomalous diffusion processes, both in the superdiffusive and subdiffusive regimes, have spurred a lot of theoretical research effort, along with experimental validation, for decades now. Their description, however, strongly relies on the existence of a metric in continuous space. Complex networks lack an intrinsic metric definition and, in this talk, I will present some theoretical "recipes" to work around this issue and recover such regimes on networks as well. On the applied side, some machine learning algorithms, like the celebrated Page Rank, exploit diffusion for classification and ranking tasks. Thus I will show how, through enhanced diffusion regimes, it is possible to address and correct some shortcomings of those algorithms and improve classification performance.

08.08.2018 - 10:15
B 016