Predicting relevant empty spots in social interaction

TL;DR

This paper introduces a heuristic method to predict relevant empty spots in social interaction records, aiming to identify unobserved individuals in social networks, demonstrated through simulation experiments.

Contribution

It presents a novel heuristic predictor function approach for identifying empty spots in social interaction data, applicable to homogeneous and inhomogeneous networks.

Findings

High precision in predicting empty spots in simulated homogeneous networks

Effective in identifying unrecorded individuals in social interactions

Demonstrates potential for analyzing incomplete social network data

Abstract

An empty spot refers to an empty hard-to-fill space which can be found in the records of the social interaction, and is the clue to the persons in the underlying social network who do not appear in the records. This contribution addresses a problem to predict relevant empty spots in social interaction. Homogeneous and inhomogeneous networks are studied as a model underlying the social interaction. A heuristic predictor function approach is presented as a new method to address the problem. Simulation experiment is demonstrated over a homogeneous network. A test data in the form of baskets is generated from the simulated communication. Precision to predict the empty spots is calculated to demonstrate the performance of the presented approach.