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