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This paper proposes a method to detect change points in dynamic socialnetworks using Fr\'echet statistics. We address two main questions: (1) whatmetric can quantify the distances between graph Laplacians in a dynamic networkand enable efficient computation, and (2) how can the Fr\'echet statistics beextended to detect multiple change points while maintaining the significancelevel of the hypothesis test? Our solution defines a metric space for graphLaplacians using the Log-Euclidean metric, enabling a closed-form formula forFr\'echet mean and variance. We present a framework for change point detectionusing Fr\'echet statistics and extend it to multiple change points with binarysegmentation. The proposed algorithm uses incremental computation for Fr\'echetmean and variance to improve efficiency and is validated on simulated and tworeal-world datasets, namely the UCI message dataset and the Enron emaildataset.

Fr\'echet Statistics Based Change Point Detection in Dynamic Social Networks