Maria Tomasso
ABSTRACT
We introduce a new ground-truth recovery boosting approach on antagonistic networks using the status-influence space obtained by the frustration cloud --- a generalization of the frustration index which models nearest consensus-based states of a signed graph. A spectral clustering and k-means approach are both examined and are compared to existing clustering methodologies on two sentiment-based datasets. We demonstrate that our approach successfully recovers all community labels on a highly modular dataset and outperforms the leading clustering technique by a factor of 3.08 on a more complex network. Additionally, we demonstrate that status and influence, in combination with network data, can be used to detect and characterize anomalous outcomes in promotion networks.
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Digital Library
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Index Terms
- Cluster boosting and data discovery in social networks
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