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Current approaches for modeling propagation in networks (e.g., spread ofdisease) are unable to adequately capture temporal properties of the data suchas order and duration of evolving connections or dynamic likelihoods ofpropagation along these connections. Temporal models in evolving networks arecrucial in many applications that need to analyze dynamic spread. For example,a disease-spreading virus has varying transmissibility based on interactionsbetween individuals occurring over time with different frequency, proximity,and venue population density. To capture such behaviors, we first develop theTemporal Independent Cascade (T-IC) model and propose a novel spread function,that we prove to be submodular, with a hypergraph-based sampling strategy thatefficiently utilizes dynamic propagation probabilities. We then introduce thenotion of 'reverse spread' using the proposed T-IC processes, and developsolutions to identify both sentinel/detector nodes and highly susceptiblenodes. The proven guarantees of approximation quality enable scalable analysisof highly granular temporal networks. Extensive experimental results on avariety of real-world datasets show that the proposed approach significantlyoutperforms the alternatives in modeling both if and how spread occurs, byconsidering evolving network topology as well as granular contact/interactioninformation. Our approach has numerous applications, including its utility forthe vital challenge of monitoring disease spread. Utilizing the proposedmethods and T-IC, we analyze the impact of various intervention strategies overreal spatio-temporal contact networks. Our approach is shown also to be highlyeffective in quantifying the importance of superspreaders, designing targetedrestrictions for controlling spread, and backward contact tracing.

Temporal Cascade Model for Analyzing Spread in Evolving Networks with Disease Monitoring Applications