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In this paper, we present a method for analyzing a dynamic network of keyfigures in the U.S.-North Korea relations during the first two quarters of2018. Our method constructs key figure networks from U.S. news articles onNorth Korean issues by taking co-occurrence of people's names in an article asa domain-relevant social link. We call a group of people that co-occurrepeatedly in the same domain (news articles on North Korean issues in ourcase) "key figures" and their social networks "key figure networks." We analyzeblock-structure changes of key figure networks in the U.S.-North Korearelations using a Bayesian hidden Markov multilinear tensor model. The resultsof our analysis show that block structure changes in the key figure network inthe U.S.-North Korea relations predict important game-changing moments in theU.S.-North Korea relations in the first two quarters of 2018.

Twists and Turns in the US-North Korea Dialogue: Key Figure Dynamic Network Analysis using News Articles