Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Interacting viruses in networks: can both survive?
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting epidemics using highly noisy data
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
Recovering information recipients in social media via provenance
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Epidemiological modeling of news and rumors on Twitter
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Seeking provenance of information using social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A tool for assisting provenance search in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with the popular susceptible-infected (SI) model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term rumor centrality. We establish that this is a maximum likelihood (ML) estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has nontrivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops of the true source across different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like.