  This paper proposes a method to detect change points in dynamic social
networks using Fr\'echet statistics. We address two main questions: (1) what
metric can quantify the distances between graph Laplacians in a dynamic network
and enable efficient computation, and (2) how can the Fr\'echet statistics be
extended to detect multiple change points while maintaining the significance
level of the hypothesis test? Our solution defines a metric space for graph
Laplacians using the Log-Euclidean metric, enabling a closed-form formula for
Fr\'echet mean and variance. We present a framework for change point detection
using Fr\'echet statistics and extend it to multiple change points with binary
segmentation. The proposed algorithm uses incremental computation for Fr\'echet
mean and variance to improve efficiency and is validated on simulated and two
real-world datasets, namely the UCI message dataset and the Enron email
dataset.
