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Influential node detection is a central research topic in social networkanalysis. Many existing methods rely on the assumption that the networkstructure is completely known \textit{a priori}. However, in many applications,network structure is unavailable to explain the underlying informationdiffusion phenomenon. To address the challenge of information diffusionanalysis with incomplete knowledge of network structure, we develop amulti-task low rank linear influence model. By exploiting the relationshipsbetween contagions, our approach can simultaneously predict the volume (i.e.time series prediction) for each contagion (or topic) and automaticallyidentify the most influential nodes for each contagion. The proposed model isvalidated using synthetic data and an ISIS twitter dataset. In addition toimproving the volume prediction performance significantly, we show that theproposed approach can reliably infer the most influential users for specificcontagions.

Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models