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Social networks are the major routes for most individuals to exchange theiropinions about new products, social trends and political issues via theirinteractions. It is often of significant importance to figure out who initiallydiffuses the information, ie, finding a rumor source or a trend setter. It isknown that such a task is highly challenging and the source detectionprobability cannot be beyond 31 percent for regular trees, if we just estimatethe source from a given diffusion snapshot. In practice, finding the sourceoften 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 thesource. In this paper, we consider two kinds of querying: (a) simple batchquerying and (b) interactive querying with direction under the assumption thatqueries can be untruthful with some probability. We propose estimationalgorithms for those queries, and quantify their detection performance and theamount of extra budget due to untruthfulness, analytically showing thatquerying significantly improves the detection performance. We perform extensivesimulations to validate our theoretical findings over synthetic and real-worldsocial network topologies.

Rumor Source Detection under Querying with Untruthful Answers