Source inference for misinformation spreading on hypergraphs

Source inference aims at revealing the seed of the misinformation spreading on social networks, and attracted great attention in the field of network science and cybersecurity. Extensive real-world data analyses have certificated that individual interactions exist pairwise and higher-order interactions, and thus should be described using the hypergraph. Previous studies about the source inference algorithms are mainly focused on simple graphs (i.e., a graph only has pairwise interactions) while neglecting the higher-order interactions. In this article, we propose a dynamical message-passing (DMP) algorithm to infer the misinformation spreading on hypergraphs. As a comparison, we also extend jordan centrality (JC), betweenness centrality (BC), closeness centrality (CC), and eigenvector centrality (EIG) algorithm to hypergraphs. The results show that our proposed DMP algorithm can accurately and effectively infer the propagation source on both artificial and real-world hypergraphs compared to the centrality algorithms. Even if the increase in propagation scale renders the other centrality algorithms completely ineffective, the DMP algorithm can still distinguish the real propagation sources in the form of a rank smaller than 10% of the network size. In general, our DMP algorithm provides an effective solution for inferring the misinformation source on high-order networks.