We’re almost done with the analyses and we’re making the final visualization of the network. As I previously posted, the network was clustered and visualized by time points. After that, we have done several more analyses and here I report how we visualized them. I’m going to post more about how we did the analyses separately. First, the nodes are grouped into experimental and not experimental (PCSF nodes). This can easily be done by parsing experimental network output and network outputs of PCSF.

Assume you have a large network and you want to find k-cores of each node and also you want to compute clustering coefficient for each one. Python package NetworkX comes with very nice methods for you to easily do these. k-core is a maximal subgraph whose nodes are at least k degree [1]. To find k-cores: Add all edges you have in your network in a NetworkX graph, and use core_number method that gets graph as the single input and returns node – k-core pairs.