Maoyuan Sun, Peng Mi, Nikolaj Tatti, Naren Ramakrishnan, Chris North


Modern visual analytic tools promote human-in-the-loop analysis but are limited in their ability to direct the user toward interesting and promising directions of study. This problem is especially acute when the analysis task is exploratory in nature, e.g., the discovery of potentially coordinated relationships in massive text datasets. Such tasks are very common in domains like intelligence analysis and security forensics where the goal is to uncover surprising coalitions bridging multiple types of relations. We introduce new maximum entropy models to discover surprising chains of relationships leveraging count data about entity occurrences in documents. These models are embedded in a visual analytic system called MERCER (Maximum Entropy Relational Chain ExploRer) that treats relationship bundles as first class objects and directs the user toward promising lines of inquiry. We demonstrate how user input can judiciously direct analysis toward valid conclusions, whereas a purely algorithmic approach could be led astray. Experimental results on both synthetic and real datasets from the intelligence community are presented.


Chris North

Maoyuan Sun

Peng Mi

Naren Ramakrishnan

Publication Details

Date of publication:
January 31, 2018
ACM Transactions on Knowledge Discovery from Data (TKDD)
Page number(s):
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Publication note:

Hao Wu, Maoyuan Sun, Peng Mi, Nikolaj Tatti, Chris North, Naren Ramakrishnan: Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models. ACM Trans. Knowl. Discov. Data 12(1): 7:1-7:34 (2018)