We have introduced the Hamming-Ipsen-Mikhailov (HIM) distance as a new data science tool to quantify difference between networks with the same vertices, such as co-expression or meta-omics networks. In computational biology, the HIM distance can be used to evaluate local and structural changes together, e.g. revealing when progression of disease or development have stronger effects. At the same time, the distance is useful to study reproducibility of network reconstruction algorithms or to mine importance of clinical phenotypes. The method will be illustrated with examples with networks derived from developmental functional genomics, immuno-oncology and metagenomics. The HIM metrics comes with an open Source implementation within the R package nettols and its web analytics interface ReNette.