Before revision:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states for a given network. The frustration cloud resolves the consensus problem with minimal sentiment disruption, taking all possible consensus views over the entire network into consideration. A frustration cloud based approach removes the brittleness of traditional network graph analysis, as it allows one to examine the consensus on entire graph . A spanning-tree-based balancing algorithm captures the variations of balanced states and global consensus of the network, and enables us to measure vertex influence on consensus and strength of its expressed attitudes. The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and  survey data .
After revision:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states . We introduce the frustration cloud as a set of all nearest balanced states, and use a graph balancing algorithm to find all nearest balanced states in deterministic way. Computational concerns are addressed by measuring consensus probabilistically, and we introduce new vertex and survey data .