Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology

@article{Hajij2018VisualDO,
  title={Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology},
  author={Mustafa Hajij and Bei Wang and Carlos Eduardo Scheidegger and Paul Rosen},
  journal={2018 IEEE Pacific Visualization Symposium (PacificVis)},
  year={2018},
  pages={125-134}
}
Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we… CONTINUE READING