Social influence source locating based on network sparsification and stratification

With the rapid growth of the internet, social networks provide an ideal platform for information exchange and propagation. Meanwhile, negative information, such as fake news, rumors, and computer viruses, often spread in social networks. To restrain the propagation of such negative information, we must find the sources of the negative influence. However, in real world applications, we usually only know the scope of the negative influence spreading and do not know who first propagates the negative influence. However, we can identify the sources of the negative influence based on the information of some observed nodes that are negatively influenced. We define this as the influencing source location problem. In this work, we present a network sparsification and stratification-based method to effectively locate multiple propagation sources using information from a few observed nodes. To reduce the complexity of the problem, we first sparsify the network by removing some edges that do not significantly impact the influence propagation to the observed nodes. We then define the stratified propagation graph where the nodes are divided into several levels according to their degrees and the paths leading to the observed nodes. We propose a method for constructing the stratified propagation graph and calculating the likelihoods of the nodes being the sources influencing the observed nodes. Then, k nodes with the maximum likelihoods are selected as the sources. Abundant experimental results show that the influence sources identified by the proposed method can influence more observed nodes at a more accurate time than other algorithms.

Automated summary

With the rapid growth of the internet, social networks have become an important platform for information exchange and propagation. However, negative information also spreads in social networks, causing a lot of problems. This study proposes a method that combines network sparsification and stratification to effectively locate the sources of negative influence using information from a few observed nodes. Experimental results demonstrate that this method can accurately identify the sources of influence in social networks, outperforming other algorithms.