Social balance theory defines balance or imbalance of sentiment relation in relations with social network theory. Sentiments can result in the emergence of two groups, where discrepancy can exist between two groups. Social and content networks model the community dynamics. We ask the question: "How can we describe and identify inequity and bias in a community?" We answer with an innovative scalable graph processing algorithm that scales social balance theory to automatically quantify disbalance and to predict outcomes in social and content networks.
Social network constructed from Wikipedia dataset of over 7000 users participating in administrator election, with the known outcomes of the elections (elected or not elected). Status graph shows strong correlation with election outcome. Outcome anomalies are defined relative to the promotional outcomes to identify questionable and lacking promotions.
We analyzed an anonymous health dataset of ≈ 12,000 doctor/patient pairs and ≈ 55 diagnoses. This method establishes a standard of care and diagnostic hierarchy from the Diagnostics Network with one thousand simulated consensus scenarios.
We provide a user friendly method to intepret results with our interactive data analytics program. You can search by individual user, select a graph traversal method, select the number of trees used to generate the metric, and even click on individual users on the graph.