Fraud risk propagation in large scale network What's the best approach to do some graph analytics and risk propagation in a network using python where multiple accounts are connected through a relationship and few of the accounts in the network are marked as bad accounts and the rest are unknown?

I tried using networkx but it seems to run forever. I have about 8MM edges and 40K nodes
You could try applying a graph convolutional network to do some semi-supervised learning. See Kipf and Welling's paper "Semi-Supervised Classification with Graph Convolutional Networks". It probably depends on how unbalanced your dataset is though. If the dataset is too large, you could train a sample of it, and train the GCN on that subset. I'd try to find some exemplar data points and create a train set from that.