Testing Spreading Behavior in Networks with Arbitrary Topologies

Abstract: Inspired by the works of Goldreich and Ron (J. ACM, 2017) and Nakar and Ron (ICALP, 2021), we initiate the study of property testing in dynamic environments with arbitrary topologies. Our focus is on the simplest non-trivial rule that can be tested, which corresponds to the 1-BP rule of bootstrap percolation and models a simple spreading behavior: Every "infected" node stays infected forever, and each "healthy" node becomes infected if and only if it has at least one infected neighbor. We show various results for both the case where we test a single time step of evolution and where the evolution spans several time steps. In the first, we show that the worst-case query complexity is $O(\Delta/\varepsilon)$ or $\tilde{O}(\sqrt{n}/\varepsilon)$ (whichever is smaller), where $\Delta$ and $n$ are the maximum degree of a node and number of vertices, respectively, in the underlying graph, and we also show lower bounds for both one- and two-sided error testers that match our upper bounds up to $\Delta = o(\sqrt{n})$ and $\Delta = O(n^{1/3})$, respectively. In the second setting of testing the environment over $T$ time steps, we show upper bounds of $O(\Delta^{T-1}/\varepsilon T)$ and $\tilde{O}(|E|/\varepsilon T)$, where $E$ is the set of edges of the underlying graph. All of our algorithms are one-sided error, and all of them are also time-conforming and non-adaptive, with the single exception of the more complex $\tilde{O}(\sqrt{n}/\varepsilon)$-query tester for the case $T = 2$.