Congestion and decongestion in a communication network

TL;DR

This paper investigates congestion in a 2D communication network, showing how increasing hub capacity and adding assortative connections based on betweenness centrality can effectively reduce congestion.

Contribution

It introduces a method to identify critical hubs using betweenness centrality and demonstrates that adding assortative connections to these hubs alleviates congestion efficiently.

Findings

Betweenness centrality effectively identifies congestion-prone hubs.

Adding assortative connections to high BC hubs relieves congestion.

Strategies to manipulate hub capacity and connections improve network flow.

Abstract

We study network traffic dynamics in a two dimensional communication network with regular nodes and hubs. If the network experiences heavy message traffic, congestion occurs due to finite capacity of the nodes. We discuss strategies to manipulate hub capacity and hub connections to relieve hub congestion. We find that the betweenness centrality (BC) criterion is useful for identifying hubs which are most likely to cause congestion, and that the addition of assortative connections to hubs of high BC relieves congestion most efficiently.