Cascade-based graph inference

Based on the work of Gomez-Rodriguez, et. al. (2011). So far, I read up to Section 3.2. First task is to understand the subroutine for identifying the MLE cascade structure from a list of hit times. Ok; putting it into practice.

I'd like to begin by repeating the authors' synthetic experiments. There are two immediate tasks:

1. Given a ground truth graph $G$, and cascade parametrization ($\beta$ and $P_c(u,v)$), generate synthetic cascades.
2. Given cascade $c$, estimate the maximum likelihood cascade tree.

Beginning the implementation... Python ("Snap.py") or Matlab? I had trouble choosing every time I began a programming assignment in CS 224w... The former has some convenient functions built-in, but I feel that there's some "barrier" between whatever I want to do and the syntactically-correct implementation; on the other hand, Matlab lets me do whatever I want, but forces me to implement everything from first principles.

Decided to proceed with Python, for its dictionary.

At the same time, try out a software for easy graph visualization (instead of drawing in my notebook manually each time):