Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios.

The role identification in social networks is important for analyzing and understanding social networks, predicting user behavior, and researching relationships and interactions between users. Most role identification works are based on link analysis following the ideas of PageRank and HITS, and some methods also combing link analysis and content analysis. However, previous role identification methods focus on the amount of topic released by the target user without considering the importance of the topic. In order to identify high-value users by topic weights in social networks, this paper first proposes a method of assigning topic weights based on the LDA model and the collective credit allocation method in science(CCA). Then we introduce content value density based on the Kullback-Leibler divergence of topic weight and topic distribution to rank the high-value users. In addition, the new method is more suitable for calculation on large-scale networks and time-evolution networks because the new method is an inductive method which works on the local graph. Finally, the experiments are carried out to analyze the effect of the topic weight allocation and content value density in a real data set. The results show that the new method is superior to the compared methods when identifying high-value users in certain circumstances.

Key nodes detection is conducive to the information promotion and public opinion monitoring. Traditional works mainly focus on the nodes affecting the breadth of information diffusion. However, there are several works concentrate on the depth of information diffusion. Breadth dictates the number of nodes receiving information in one hop propagation. Depth implies the length of the retweeting cascade of information and the number of social circles getting information. Depth is as important as breadth, for the nodes which contribute to the depth of information diffusion can promote society propagation (SP). If we have a good control of these nodes, information will ignite the community and minority will tip over the cognitive of the group. The purpose of this paper is to identify the influential nodes which can influence the SP in breadth and depth. With the analysis of SP, we proposed BNDCS (backbone node detection based on community structure) to detect these nodes. In this paper, we defined backbone nodes (BNs) by the location feature and the process of information diffusion. Besides, we presented a cohesion subgroup detection method based on hybrid seed expansion. It can adapt the network with no significant community structure and fit the star topology well. We proposed information tension factor to measure the influence of bridge nodes. Through the experiment contrasts, BNDCS is superior to compared methods. It showed that BNs play a critical role in SP. Therefore, monitoring BNs can prevent the mass incidents occurring and stop the spread of panic.

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) are two communication types in intelligent transport systems (ITSs) that can share spectrum through in-band overlay. V2V primarily ensures traffic safety, while V2N focuses on entertainment and information sharing. Ensuring reliable V2V communication and high-rate V2N in resource-constrained and dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel Graph Attention Network (GAT)-Advantage Actor-Critic (GAT-A2C) reinforcement learning (RL) framework in the paper. In the framework, we build a graph based on V2V links and their potential interference relationships, in which the V2V link between vehicles is regarded as the node, and connect nodes with interference relationships to form edges. GAT is employed to model significant interference relationships among neighboring vehicles while accounting for real-time mobility and channel conditions. The feature output by GAT and the feature of link itself are combined to form the environment state, which is then fed into the agent to optimize the allocation of resource blocks and transmission power for V2V and V2N. Simulation results demonstrate that the method significantly improves V2N rates and V2V communication success ratios across varying vehicle densities. The proposed solution exhibits high scalability, making it suitable for future large-scale intelligent vehicular networks in dynamic traffic scenarios.