In this paper, we address the personalized node ranking (PNR) problem for signed networks, which aims to rank nodes in an order most relevant to a given seed node in a signed network. The recently-proposed PNR methods introduce the concept of the signed random surfer, denoted as SRSurfer, that performs the score propagation between nodes using the balance theory. However, in real settings of signed networks, edge relationships often do not strictly follow the rules of the balance theory. Therefore, SRSurfer-based PNR methods frequently perform incorrect score propagation to nodes, thereby degrading the accuracy of PNR. To address this limitation, we propose a novel random-walk based PNR approach with sign verification, named as OBOE (lOok Before yOu lEap). Specifically, OBOE carefully verifies the score propagation of SRSurfer by using the topological features of nodes. Then, OBOE corrects all incorrect score propagation cases by exploiting the statistics of a given network. The experiments on 3 real-world signed networks show that OBOE consistently and significantly outperforms 5 competing methods with improvement up to 13%, 95%, and 249% in top-k PNR, bottom-k PNR, and troll identification tasks, respectively. All OBOE codes and datasets are available at: http://github.com/wonchang24/OBOE.

Underwater robotic vehicles (URVs) have become an important tool for various underwater tasks because they have greater speed, endurance, depth capability, and safety than human divers. Many URVs powered by electric rotors driving propellers. The thruster system is known to be nonlinear and time-varying. The system dynamics of URVs can be greatly influenced by the thruster of dynamics at low speed or station keeping. Good control of a vehicle at low speed is also an important design problem which must be solved to permit important operations like automatic docking and combined vehicle-manipulator control. The conventional linear controller based on the simplified vehicle dynamics may not be able to handle these properties and result in poor performance. This paper describes a fuzzy model-based controller of an underwater robotic vehicle with the influence of the thruster dynamics. The fuzzy controller presented in this paper is based on a Tagaki-Sugeno-Kang (TSK) fuzzy model and guarantees the stability of overall fuzzy control system. Its superiority to the conventional linear controller is investigated by computer simulation.