  In this paper, we study cascading failures in power grids through the lens of
information diffusion models. Similar to the spread of rumors or influence in
an online social network, it has been observed that failures (outages) in a
power grid can spread contagiously, driven by viral spread mechanisms. We
employ a stochastic diffusion model that is Markovian (memoryless) and local
(the activation of one node, i.e., transmission line, can only be caused by its
neighbors). Our model integrates viral diffusion principles with physics-based
concepts, by correlating the diffusion weights (contagion probabilities between
transmission lines) with the hyperparametric Information Cascades (IC) model.
We show that this diffusion model can be learned from traces of cascading
failures, enabling accurate modeling and prediction of failure propagation.
This approach facilitates actionable information through well-understood and
efficient graph analysis methods and graph diffusion simulations. Furthermore,
by leveraging the hyperparametric model, we can predict diffusion and mitigate
the risks of cascading failures even in unseen grid configurations, whereas
existing methods falter due to a lack of training data. Extensive experiments
based on a benchmark power grid and simulations therein show that our approach
effectively captures the failure diffusion phenomena and guides decisions to
strengthen the grid, reducing the risk of large-scale cascading failures.
Additionally, we characterize our model's sample complexity, improving upon the
existing bound.
