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

Asymptotic Distribution-free Change-point Detection for Modern Data Based on a New Ranking Scheme