We introduce new distance measures for comparing embedded graphs based on the Fr\'echet distance and the weak Fr\'echet distance. These distances take the combinatorial structure as well as the geometric embeddings of the graphs into account. Our distance measures are motivated by the comparison of road networks, for instance, to evaluate the quality of a map construction algorithm, a task for which suitable distance measures are currently missing. In this paper, we present a general algorithmic approach for computing these distances. Although we show that deciding the distances is NP-complete for general embedded graphs, we prove that our approach yields polynomial time algorithms for the distances based on the Fr\'echet distance if the graphs are trees, and for the distances based on the weak Fr\'echet distance if the graphs are trees or if they are plane. Furthermore , we prove that deciding the distances based on the Fr\'echet remains NP-complete for plane graphs and show how our general algorithmic approach yields an exponential time algorithm and a polynomial time approximation algorithm for this case.
We introduce new distance measures for comparing embedded graphs based on the Fr\'echet distance and the weak Fr\'echet distance. These distances take the combinatorial structure as well as the geometric embeddings of the graphs into account. We present a general algorithmic approach for computing these distances. Although we show that deciding the distances is NP-hard for general embedded graphs, we prove that our approach yields polynomial time algorithms if the graphs are trees, and for the distance based on the weak Fr\'echet distance if the graphs are planar embedded. Moreover , we prove that deciding the distances based on the Fr\'echet distance remains NP-hard for planar embedded graphs and show how our general algorithmic approach yields an exponential time algorithm and a polynomial time approximation algorithm for this case.
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Our distance measures are motivated by the comparison of road networks, for instance, to evaluate the quality of a map construction algorithm, a task for which suitable distance measures are currently missing. In this paper, we
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NP-complete
NP-hard
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for the distances based on the Fr\'echet distance
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distances
distance
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trees or if they are plane. Furthermore
planar embedded. Moreover
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remains NP-complete for plane
distance remains NP-hard for planar embedded