CLP: A Community based Label Propagation Framework for Multiple Source Detection

Given an aftermath of an information spreading, i.e., an infected network GNafter the propagation of malicious rumors, malware or viruses, how can we identify the sources of the cascade? Answering this problem, which is known as the multiple source detection (MSD) problem, is critical whether for forensic use or insights to prevent future epidemics.Despite the recent considerable effort, most of them are built on a preset propagation model, which limits their application range. Some attempts aim to break this limitation via a label propagation scheme where the nodes surrounded by a large proportion of infected nodes are highlighted. Nonetheless, the detection accuracy may suffer since the node labels are simply integers with all infected or uninfected nodes sharing the same initialization setting respectively, which fall short of sufficiently distinguishing their structural properties. To this end, we propose a community based label propagation (CLP) framework that locates multiple sources through exploiting the community structures formed by infected subgraphs of different sources. Besides, CLP tries to enhance the detection accuracy by incorporating node prominence and exoneration effects, namely the nodes surrounded by larger proportions of infected nodes are more likely to be sources, and the uninfected nodes or infected ones in neighboring community hold the key in exonerating an infected node from being the source. As such, CLP is applicable in more propagation models, and is provably convergent. Experiments on both synthetic and real-world networks further validate the superiority of CLP to the state-of-the-art, boosting the F-score from few percents to approximately 68× in large-scale networks.