Table 4. .
Advantages and limitations of some selected graphs distance measures.
| Characteristics | ||||||
|---|---|---|---|---|---|---|
| Spatial location | Different size | Computational cost | Structural difference | Available Code | ||
| Methods | SimiNet (Mheich et al., 2018) | + | − | ++ | − | https://github.com/amheich/SimiNet |
| D-measure (Schieber et al., 2017) | − | − | + (not for sparse graph) | + | https://github.com/tischieber/Quantifying-Network-Structural-Dissimilarities | |
| DeltaCon (Koutra et al., 2013) | − | − | + | + | https://web.eecs.umich.edu/∼dkoutra/CODE/deltacon.zip | |
| Kernel methods (Borgwardt et al., 2005; Shervashidze et al., 2011) | − | + | − − | + | https://github.com/BorgwardtLab/GraphKernels | |
Note. Note that “−” indicates a characteristic that is not integrated in the similarity score of the method; “+” a characteristic that is integrated in the methods; “− −” for worst computational time; and “++” for very good computational time. Spatial location = physical location of nodes; Different size = graphs with different number of nodes; Computational cost = algorithm running time; Structural difference = detection of difference between node’s links in two graphs.