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Temporal Cascade Model for Analyzing Spread in Evolving Networks with Disease Monitoring Applications by Haldar, Aparajita; Wang, Shuang; Demirci, Gunduz; Oakley, Joe; Ferhatosmanoglu, Hakan is a scholarly article available to read on EtoBox.

What is Temporal Cascade Model for Analyzing Spread in Evolving Networks with Disease Monitoring Applications about?

Current approaches for modeling propagation in networks (e.g., spread of disease) are unable to adequately capture temporal properties of the data such as order and duration of evolving connections or dynamic likelihoods of propagation along these connections. Temporal models in evolving networks are crucial in many applications that need to analyze dynamic spread. For example, a disease-spreading virus has varying transmissibility based on interactions between individuals occurring over time with different frequency, proximity, and venue population density. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model and propose a novel spread function, that we prove to be submodular, with a hypergraph-based sampling strategy that efficiently utilizes dynamic propagation probabilities. We then introduce the notion of 'reverse spread' using the proposed T-IC processes, and develop solutions to identify both sentinel/detector nodes and highly susceptible nodes. The proven guarantees of approximation quality enable scalable analysis of highly granular temporal networks. Extensive experimental results on a variety of real-world datasets show that the propo

Author

Haldar, Aparajita; Wang, Shuang; Demirci, Gunduz; Oakley, Joe; Ferhatosmanoglu, Hakan

Published

2022

Language

EN