Clearing Contamination in Large Networks

TL;DR

This paper addresses the challenge of efficiently clearing contamination in large networks by modeling it as a graph searching game, providing an approximation algorithm and experimental validation on real-world networks.

Contribution

It introduces an approximation algorithm for the NP-hard graph clearing problem and demonstrates its near-optimal performance on large online networks.

Findings

Algorithm performs near optimally in experiments

Problem is NP-hard even on directed acyclic graphs

Effective in large real-world networks

Abstract

In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally.