Optimizing the Temporal Resolution in Dynamic Graph Mining
Funded by the National Geospatial-Intelligence Agency (NGA), $361k
Award information:
PI (UAlbany): Petko Bogdanov
Associate Professor
Dept. of Computer Science
1215 Western Ave., UAB 416,
Albany, NY 12222
University at Albany - SUNY
PH: 518-437-4939
FX: 518-437-4949
Email: pbogdanov@albany.edu
Duration: 9/20-8/22
Project Summary
Dynamic graph mining can elucidate the activity of in-network processes in diverse application domains from social, mobile and communication networks to infrastructure and biological networks. Compared to static graphs, the temporal information of when graph events occur is an important new dimension for improving the quality, interpretation and utility of mined patterns. However, mining dynamic graphs poses an important, though often overlooked, challenge: observed data must be analyzed at an appropriate temporal resolution (timescale), commensurate with the underlying rate of application-specific processes. The main objective of the project is to improve the quality and interpretability of dynamic graph mining results by bridging the disconnect between timescale selection and the data mining algorithms. Enabling timescale-aware methods is pivotal to improving the utility of dynamic graphs and their increasingly ubiquitous applications.
Supported Students
- Maxwell McNeil (PhD student)
- Carolyn Kaminski (PhD student)
- Boya Ma (PhD student)
- Hafez Serdah (Undegraduate Student)
Publications
“SAGA: Signal-Aware Graph Aggregation.” Maxwell McNeil, Boya Ma, and Petko Bogdanov. Proceedings of SIAM International Conference on Data Mining (SDM 2022).
“Temporal Graph Signal Decomposition.” Maxwell McNeil, Lin Zhang, and Petko Bogdanov. ACM International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2021).