  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.
