Before revision:  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 . We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions  .
After revision:  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 of up to 80,000 vertices and half-a-million edges, and we demonstrate the power of the proposed approach to provide discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions .