  Change-point detection (CPD) involves identifying distributional changes in a
sequence of independent observations. Among nonparametric methods, rank-based
methods are attractive due to their robustness and effectiveness and have been
extensively studied for univariate data. However, they are not well explored
for high-dimensional or non-Euclidean data. This paper proposes a new method,
Rank INduced by Graph Change-Point Detection (RING-CPD), which utilizes
graph-induced ranks to handle high-dimensional and non-Euclidean data. The new
method is asymptotically distribution-free under the null hypothesis, and an
analytic $p$-value approximation is provided for easy type-I error control.
Simulation studies show that RING-CPD effectively detects change-points across
a wide range of alternatives and is also robust to heavy-tailed distribution
and outliers. The new method is illustrated by the detection of seizures in a
functional connectivity network dataset and travel pattern changes in the New
York City Taxi dataset.
