Yes, I did read the paper. (Disclaimer: I have a PhD, but not in graph theory. Your results may vary.)
In short, the paper repeats analysis and numerical simulations of a simplified 'agreement model'. People are abstracted as nodes on a graph, communication happens between them, and consensus is reached. If a graph is initialized randomly, with nodes 'believing' either A or B, eventually (in log(N) time) the graph reaches consensus with every node 'believing' A xor B.
This paper adds a twist; some fraction of nodes are 'committed' to A, and cannot ever be convinced of B. To quote the paper:
Here, we study the evolution of opinions in the binary agreement model starting from an initial state where all agents
adopt a given opinion B, except for a finite fraction p of the total number of agents who are committed agents and have
state A. Committed agents, introduced previously in [23], are defined as nodes that can influence other nodes to alter their
state through the usual prescribed rules, but which themselves are immune to influence.
Now, if even one node cannot be convinced of B, then no consensus can be reached -- but it doesn't really matter. If the fraction is really small, then you can more or less ignore them.
The interesting part about that paper is their threshold effect -- once p gets to be over 10%, not only does A eventually win, but it does so -quickly-.
The applications to politics still hold, but not on the big, obvious issues. Those issues, like taxes and abortion and health care and anything else that really makes the news, have committed believers on both sides -- they're outside the scope of study. Where this research becomes really interesting is in quieter, uncontroversial issues -- like regulation details, or climage change before Al Gore. There, this research suggests that the influence of sockpuppetry and lobbying is nonlinear -- beyond a critical point, the lobbyists completely win.
Of course, caveats about "the real world isn't an abstract graph" apply.