Detecting Disruption: Identifying Structural Changes in the Verkhovna Rada

Tom Magelinski, PhD, Jialin Hou, Tymofiy Mylovanov, Kathleen M. Carley
June 2019
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Abstract

We identify time periods of disruption in the voting patterns of the Ukrainian parliament for the last three convocations. We compare two methods: ideal point estimation (PolSci) and faction detection (CS). Both methods identify the revolution in Ukraine in 2014. The faction detection method also detects structural changes prior to the revolution (election of the president whose tenure was ended early by the revolution), while the ideal points method performs stronger after 2014, identifying a disruption around voting on constitutional changes to implement Minsk II agreements between separatists and Ukraine. The ideal point method is better at detecting position changes of the members of parliament, while the faction method is better at detecting changes in relationships between different MPs. The results suggest that after 2014, the Ukrainian parliament has become more consolidated, but the distribution of its political positions continues to evolve in response to changes in geo-political conditions.

Type
Conference paper
Publication
In SBP-BRiMS 2019
Community Detection Network Dynamics Voting Networks
Tom Magelinski, PhD
Tom Magelinski, PhD
Senior Data Scientist - Information Extraction and Generative AI

I build AI systems that help domain experts understand vast amounts of data through state-of-the-art techniques from natural language processing, generative modeling, graph ML, and network science. I’m particularly interested researching and developing techniques to combine NLP and graph-based approaches to capture complex relationships in unstructured data.

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