  In this paper, I present a method to solve a node discovery problem in a
networked organization. Covert nodes refer to the nodes which are not
observable directly. They affect social interactions, but do not appear in the
surveillance logs which record the participants of the social interactions.
Discovering the covert nodes is defined as identifying the suspicious logs
where the covert nodes would appear if the covert nodes became overt. A
mathematical model is developed for the maximal likelihood estimation of the
network behind the social interactions and for the identification of the
suspicious logs. Precision, recall, and F measure characteristics are
demonstrated with the dataset generated from a real organization and the
computationally synthesized datasets. The performance is close to the
theoretical limit for any covert nodes in the networks of any topologies and
sizes if the ratio of the number of observation to the number of possible
communication patterns is large.
