Fast Change Point Detection on Dynamic Social Networks
This work addresses the need for efficient change point detection in dynamic networks, which is crucial for applications in sociology, biology, and communication, but it appears incremental as it builds on existing snapshot models.
The paper tackled the problem of change point detection in dynamic networks under the snapshot model, achieving up to 9X speedup over state-of-the-art methods while improving quality on synthetic and real-world networks.
A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.