  Structural balance modeling for signed graph networks presents how to model
the sources of conflicts. The state-of-the-art has focused on computing the
frustration index of a signed graph as a critical step toward solving problems
in social and sensor networks and for scientific modeling. However, the
proposed approaches do not scale to modern large, sparse signed networks. Also,
they do not address that there is more than one way in some networks to reach a
consensus with the minimum number of edge-sign switches needed. We propose an
efficient balanced state discovery algorithm and a network frustration
computation that will discover the nearest balanced state for the \emph{any}
size of the graph network and compute the frustration of the network. The
speedup of the proposed method is around 300 times faster than the
state-of-the-art for signed graphs with hundreds of thousands of edges. The
technique successfully scales to find the balanced states and frustration of
the networks with millions of nodes and edges in real time where
state-of-the-art fails.
