Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics
Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node activation times. Applications seldom supply these, and inference must instead proceed through indirect and noisy proxies for the terminal infection states. We study this inverse problem on a fixed, known graph under the Hidden Independent Cascade (HIC) model, with one spreading probability per node rather than a single global rate, so the number of unknowns scales with the number of nodes. Seed sets and observation parameters are known, while activation times and terminal infection states are latent, and the observed-data likelihood requir...
| Authors | Anubha Goel, Illia Oleksiienko, Mateusz Wilinski et al. |
|---|---|
| Primary category | cs.SI |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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