Predicting triadic closure in networks using communicability distance functions

TL;DR

This paper introduces a communication-based method using communicability distance functions to predict triadic closure in complex networks, outperforming random predictions and providing insights into network communication optimization.

Contribution

It presents a novel mathematically formulated approach for predicting triadic closure based on communication quality, validated on real-world networks.

Findings

Predicts 20% of triadic closures compared to 7.6% by random

Outperforms random in explaining clustering coefficient and path length

Reveals features relevant to optimizing network communication

Abstract

We propose a communication-driven mechanism for predicting triadic closure in complex networks. It is mathematically formulated on the basis of communicability distance functions that account for the quality of communication between nodes in the network. We study $25$ real-world networks and show that the proposed method predicts correctly $20\%$ of triadic closures in these networks, in contrast to the $7.6\%$ predicted by a random mechanism. We also show that the communication-driven method outperforms the random mechanism in explaining the clustering coefficient, average path length, and average communicability. The new method also displays some interesting features with regards to optimizing communication in networks.