SDSI: Source Detection in Structurally Incomplete Social Networks

The dissemination of rumors imposes substantial hazards. Therefore, accurately identifying the source of rumor and promptly controlling information propagation hold paramount practical significance. Presently, prevailing sensor deployment methods solely focus on network structural information, disregarding the propagation process, thus incurring certain limitations. Additionally, source detection methods presuppose reliable assumptions, i.e., complete network structure and observational data. However, due to temporal constraints and cost considerations, the acquired network information is often structurally incomplete: partial edges missing. To address these issues, this paper introduces a novel approach, namely <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>ource <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b>etection in <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>tructurally <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b>ncomplete social networks (SDSI). Firstly, to monitor the network efficiently, a certain number of sensors are deployed using quality-guaranteed Monte Carlo simulations to achieve maximum coverage. In the source detection phase, considering the acquired incomplete information, the source node is determined based on Bayesian posterior maximum estimation. Additionally, SDSI is enhanced through incorporating the sharing counts of the information in social networks. Extensive experiments in diverse scenarios demonstrate the superiority and robustness of the proposed SDSI.