Role discovery assist in various applications of online social networks, such as water army detection, shopping recommendation, rumor tracing, etc. However, existing studies often overlook the significance of hierarchical structures in online social networks, which are crucial for understanding the roles played by different users. To address this gap, we propose a novel approach based on hyperbolic graph learning, called HyperRole, which effectively leverages the hierarchical structure of online social networks for role discovery. HyperRole first extracts structural features from users and constructs user sequences based on feature similarity, capturing the relationships between users across different scales. Then, we learn role information from structural features by hyperbolic graph Transformer to embed users into the hyperbolic space, preserving the hierarchical structure between users and enabling interactions between users of the same level that are far away from each other. Additionally, we leverage the hierarchical distance between the target user and other users within the same sequence to guide and modify the role information of the target user. Based on the generated user role embeddings, we train a multi-class classifier to classify roles. Extensive experiments on several real-world network datasets demonstrate that our model outperforms existing baseline methods, showcasing its superior performance.

Joint Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) offloading presents an efficient approach to leverage surplus computing resources from neighboring devices, thereby expanding the coverage of computing resources supply in the context of the Internet of Vehicles. However, many studies overlook the significance of topological communications caused by the rapid movement of vehicles, privacy, and communication intentions. To achieve efficient task offloading when facing various topological link structures, we first propose a novel topological link-aware task co-offloading (TLCO) method designed for partially offloading in the joint V2V and V2I system. Next, we model the sequential subtasks offloading process as the Markov Decision Process (MDP) and utilize the Double Deep Q-Network (DDQN) algorithm to optimize the total delay of the proposed system. Additionally, we put forth a prediction framework named Sliding Time Windows and TLCO algorithm (STW-TLCO) to accurately forecast the computation load at various time windows using pulsed parameters. Extensive experimental results demonstrate the effectiveness and superiority of the proposed TLCO-DDQN algorithm in comparison to other Deep Reiforcement Learning (DRL)-based and Greedy-based approaches. Furthermore, the STW-TLCO algorithm exhibits high accuracy, with an R-squared value exceeding 96%, confirming its predictive capabilities.