• search hit 4 of 23
Back to Result List

Comparing temporal graphs using dynamic time warping

  • Within many real-world networks, the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic tem- poral graph warping (dtgw) distance to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) NP-hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-world data, we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Vincent Froese, Brijnesh JainORCiD, Rolf Niedermeier, Malte Renken
URN:urn:nbn:de:bvb:898-opus4-38493
DOI:https://doi.org/10.1007/s13278-020-00664-5
Parent Title (English):Social Network Analysis and Mining
Publisher:Springer Nature
Document Type:Article
Language:English
Year of first Publication:2020
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/04/30
Volume:10
Article Number:50
First Page:1
Last Page:16
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
research focus:Digitalisierung
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International