Before revision:  Because graphlets are applicable to many other network science problems, we provide an implementation of our heterogeneous graphlet counting approach (available upon request) .
After revision:  Then, we find high-scoring alignments with respect to our heterogeneous node similarity and edge conservation measures. In evaluations on synthetic networks and real-world biological network data, our proposed heterogeneous NA methods lead to higher-quality alignments and better robustness to noise in the data than their homogeneous counterparts .