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Rumor source identification in large social networks has received significantattention lately. Most recent works deal with the scale of the problem byobserving a subset of the nodes in the network, called sensors, to estimate thesource. This paper addresses the problem of locating the source of a rumor inlarge social networks where some of these sensor nodes have failed. We estimatethe missing information about the sensors using doubly non-negative (DN) matrixcompletion and compressed sensing techniques. This is then used to identify theactual source by using a maximum likelihood estimator we developed earlier, ona large data set from Sina Weibo. Results indicate that the estimationtechniques result in almost as good a performance of the ML estimator as forthe network for which complete information is available. To the best of ourknowledge, this is the first research work on source identification withincomplete information in social networks.

Identification of Source of Rumors in Social Networks with Incomplete Information