Node discovery in a networked organization

TL;DR

This paper introduces a mathematical method for discovering covert nodes in networked organizations by analyzing suspicious logs, achieving near-theoretical performance limits across various network topologies and sizes.

Contribution

It develops a maximal likelihood estimation model for identifying covert nodes and suspicious logs in social networks, enhancing detection accuracy in covert node discovery.

Findings

High precision and recall in detecting covert nodes

Performance close to theoretical limits with sufficient observation data

Effective across diverse network topologies and sizes

Abstract

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.