SCube: A Tool for Segregation Discovery

TL;DR

SCube is a tool that helps discover social segregation patterns in large relational and graph data by providing a multi-dimensional data cube for exploratory analysis, demonstrated through case studies on gender occupational segregation.

Contribution

The paper introduces SCube, a novel system combining attributed graph clustering and frequent itemset mining for segregation discovery in complex social datasets.

Findings

Effective identification of segregation contexts in large datasets

Demonstrated scalability on real-world datasets from Italy and Estonia

Insights into gender occupational segregation patterns

Abstract

Segregation is the separation of social groups in the physical or in the online world. Segregation discovery consists of finding contexts of segregation. In the modern digital society, discovering segregation is challenging, due to the large amount and the variety of social data. We present a tool in support of segregation discovery from relational and graph data. The SCube system builds on attributed graph clustering and frequent itemset mining. It offers to the analyst a multi-dimensional segregation data cube for exploratory data analysis. The demonstration first guides the audience through the relevant social science concepts. Then, it focuses on scenarios around case studies of gender occupational segregation. Two real and large datasets about the boards of directors of Italian and Estonian companies will be explored in search of segregation contexts. The architecture of the SCube system and its computational efficiency challenges and solutions are discussed.