Large Graphs: NetworkX distributed alternative
Good , old and unsolved question! Distributed processing of large graphs as far as I know (speaking as a graph guy) has 2 different approaches, with the knowledge of Big Data frameworks or without it.

SNAP library from Jure Leskovec group at  Stanford which is originally in C++ but also has a Python API (please check if you need to use C++ API or Python does the job you want to do). Using snap you can do many things on massive networks without any special knowledge of Big Data technologies. So I would say the easiest one.

Using Apache Graphx is wonderful only if you have experience in Scala because there is no Python thing for that. It comes with a large stack of built in algorithms including centrality measures. So the second easiest in case you know Scala.

Long time ago when I looked at GraphLab it was commercial. Now I see it goes open source so maybe you know better than me but from my out-dated knowledge I remember that it does not support a wide range of algorithms and if you need an algorithm which is not there it might get complicated to implement. On the other hand it uses Python which is cool. After all please check it again as my knowledge is for 3 years ago.

If you are familiar with Big Data frameworks and working with them, Giraph and Gradoop are 2 great options. Both do fantastic jobs but you need to know some Big Data architecture e.g. working with a hadoop platform.

PS

1) I have used simple NetworkX and multiprocessing to distributedly process DBLP network with 400,000 nodes and it worked well, so you need to know HOW BIG your graph is.

2) After all, I think SNAP library is a handy thing.