Link prediction in time-evolving organizational social networks

Abstract

A time-evolving (or dynamic) social network is a network whose structure may change over time. The observed changes may refer to entities (for example, new members may join or existing members may leave the network) or connections between them (new relationships may be established or existing relationships may be broken). A number of studies have focused on the empirical analysis of time evolving networks to examine their properties. In this paper, the problem of link prediction is considered. It aims at predicting future connections between pairs of unconnected links based on existing current connections. A social network considered in the paper is an organizational social network, meant as a network where nodes represent employees or units of the organization, and edges between nodes represent formal or informal relations or information flow between them. The structure of our network is formed by digital communication between employees. The aim of the paper is to experimentally verify to what extent, if any, the similarity-based methods based on local or global information may support the process of predicting potential new (non-existent yet) connections between employees in a time-evolving organizational social network. Ten similarity methods based on local and global information have been considered and tested. As a result, the best similarity methods (candidates to be considered as good predictors of these future connections), have been suggested.