Detecting Privileged Documents by Ranking Connected Network Entities

TL;DR

This paper introduces a link analysis method that constructs a network of human entities from email metadata to identify privileged documents by ranking entities based on their interactions with legal professionals.

Contribution

It proposes a novel network-based scoring algorithm that leverages entity interactions to improve privileged document detection in email data.

Findings

Effective ranking of legal entities for privileged document detection

Improved identification accuracy over baseline methods

Demonstrated success on experimental email datasets

Abstract

This paper presents a link analysis approach for identifying privileged documents by constructing a network of human entities derived from email header metadata. Entities are classified as either counsel or non-counsel based on a predefined list of known legal professionals. The core assumption is that individuals with frequent interactions with lawyers are more likely to participate in privileged communications. To quantify this likelihood, an algorithm assigns a score to each entity within the network. By utilizing both entity scores and the strength of their connections, the method enhances the identification of privileged documents. Experimental results demonstrate the algorithm's effectiveness in ranking legal entities for privileged document detection.