Path-based multi-sources localization in multiplex networks

With the prosperity of modern technology, propagation phenomena of diverse information become universal nowadays. Nevertheless, spreading of malicious information will inevitably bring undesired harm or economic losses. These spreading phenomena are usually triggered by limited sources; therefore, it is of great significance to locate these sources to avoid further losses. With the emergence of various social platforms, social networks seem to be integrated. Hence, multiplex networks are desirable to mimic the properties of integrated social networks. Source localization problems on single-layer networks are studied by various scholars whereas less attention has been paid to corresponding problems on multiplex networks. Regarding this, we propose a source locating method in this manuscript, named path-based source localization on multiplex networks (PSLM). With the adoption of the source centrality theory, we apply a label iteration process in order to find nodes with the largest local labels which are regarded as the sources. Furthermore, high uncertainty of spreading path will be incurred by the low spreading probability. Aiming to address such uncertainty, observers are deployed in advance to record the spreading directions. Then, extensive experiments are performed on selected datasets and we find PSLM outperforms the existing ones. Moreover, we also study the effects of various factors on the locating accuracy and find that the locating accuracy improves with the increase of the inter-layer spreading rate. (c) 2022 Published by Elsevier Ltd.