Rumor Source Detection under Querying with Untruthful Answers

TL;DR

This paper investigates rumor source detection in social networks using querying strategies, accounting for untruthful answers, and demonstrates that querying significantly enhances detection accuracy through theoretical analysis and simulations.

Contribution

It introduces new algorithms for source detection with untruthful queries and quantifies their effectiveness and additional costs analytically.

Findings

Querying improves detection probability significantly.

Untruthfulness increases the required querying budget.

Algorithms outperform baseline methods in simulations.

Abstract

Social networks are the major routes for most individuals to exchange their opinions about new products, social trends and political issues via their interactions. It is often of significant importance to figure out who initially diffuses the information, ie, finding a rumor source or a trend setter. It is known that such a task is highly challenging and the source detection probability cannot be beyond 31 percent for regular trees, if we just estimate the source from a given diffusion snapshot. In practice, finding the source often entails the process of querying that asks "Are you the rumor source?" or "Who tells you the rumor?" that would increase the chance of detecting the source. In this paper, we consider two kinds of querying: (a) simple batch querying and (b) interactive querying with direction under the assumption that queries can be untruthful with some probability. We propose estimation algorithms for those queries, and quantify their detection performance and the amount of extra budget due to untruthfulness, analytically showing that querying significantly improves the detection performance. We perform extensive simulations to validate our theoretical findings over synthetic and real-world social network topologies.