FROST: Controlled Label Propagation for Multisource Detection

Abstract

We often see rumors rapidly spreading in online social networks. These are harmful for our society in many ways. Infection source detection is the task of identifying the sources of rumors or any other such infections in social networks, so that appropriate intervention could be performed to control the harm. Researchers have studied this problem under various scenarios, where multisource detection has been of special importance. In this article, we propose a novel infection rate controlled label propagation method for multisource detection called FROST . It leverages the connection strengths between a pair of nodes in the form of infection rate to capture the implicit information latent within an infection. Initially, labels are assigned to nodes indicating whether the nodes are infected or not. Afterward, the labels are propagated across the network in a controlled manner based on the infection rate. Once the propagation converges, the locally prominent nodes are considered as sources. We compare FROST against six state-of-the-art methods and two heuristic baselines in terms of ten evaluation measures over four social networks datasets. Our results show that FROST generally outperforms the competing methods across various evaluation measures and datasets. It also estimates the number of sources closer to the actual than the competing methods. FROST scales effectively for large infections, including when there are infection overlaps, where the competing methods generally lag.