Inferring Company Structure from Limited Available Information

TL;DR

This paper introduces algorithms to deduce company organizational structures using minimal data, focusing on pairwise employee relationships and limited hierarchy details.

Contribution

It presents novel dynamic programming and greedy algorithms tailored for inferring company structures from sparse information.

Findings

Algorithms effectively infer company hierarchies with limited data

Dynamic programming and greedy methods outperform baseline approaches

The techniques are applicable to real-world organizational analysis

Abstract

In this paper we present several algorithmic techniques for inferring the structure of a company when only a limited amount of information is available. We consider problems with two types of inputs: the number of pairs of employees with a given property and restricted information about the hierarchical structure of the company. We provide dynamic programming and greedy algorithms for these problems.