Comparing Temporal Graphs Using Dynamic Time Warping
arXiv:1810.06240 · doi:10.1007/s13278-020-00664-5
Abstract
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 temporal graph warping distance (dtgw) 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-word data we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.
References in corpus (11)
- Temporal Networks
- De-anonymizing Social Networks
- High-resolution measurements of face-to-face contact patterns in a primary school
- The fundamental advantages of temporal networks
- A Survey on Graph Kernels
- Temporal Graphs
- Can co-location be used as a proxy for face-to-face contacts?
- Mitigation of infectious disease at school: targeted class closure vs school closure
- On Valid Optimal Assignment Kernels and Applications to Graph Classification
- A Family of Tractable Graph Distances
- Dynamic Time Warping in Strongly Subquadratic Time: Algorithms for the Low-Distance Regime and Approximate Evaluation