Hierarchical Change Point Detection on Dynamic Networks

Yu Wang, Aniket Chakrabarti, David Sivakoff, Srinivasan Parthasarathy

This paper studies change point detection on networks with community structures. It proposes a framework that can detect both local and global changes in networks efficiently. Importantly, it can clearly distinguish the two types of changes. The framework design is generic and as such several state-of-the-art change point detection algorithms can fit in this design. Experiments on both synthetic and real-world networks show that this framework can accurately detect changes while achieving up to 800X speedup.

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