Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes. In recent years, the appearance of graph neural networks has significantly improved the accuracy of graph embedding. However, the influence of clusters was not considered in existing graph neural network (GNN)-based methods, so this paper proposes a new method to incorporate the influence of clusters into the generation of graph embedding. We use the attention mechanism to pass the message of the cluster pooled result and integrate the whole process into the graph autoencoder as the third layer of the encoder. The experimental results show that our model has made great improvement over the baseline methods in the node clustering and link prediction tasks, demonstrating that the embeddings generated by our model have excellent expressiveness.

Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light timing optimization method based on double dueling deep Q-network, MaxPressure, and Self-organizing traffic lights (SOTL), namely EP-D3QN, which controls traffic flows by dynamically adjusting the duration of traffic lights in a cycle, whether the phase is switched based on the rules we set in advance and the pressure of the lane. In EP-D3QN, each intersection corresponds to an agent, and the road entering the intersection is divided into grids, each grid stores the speed and position of a car, thus forming the vehicle information matrix, and as the state of the agent. The action of the agent is a set of traffic light phase in a signal cycle, which has four values. The effective duration of the traffic lights is 0–60 s, and the traffic light phases switching depends on its press and the rules we set. The reward of the agent is the difference between the sum of the accumulated waiting time of all vehicles in two consecutive signal cycles. The SUMO is used to simulate two traffic scenarios. We selected two types of evaluation indicators and compared four methods to verify the effectiveness of EP-D3QN. The experimental results show that EP-D3QN has superior performance in light and heavy traffic flow scenarios, which can reduce the waiting time and travel time of vehicles, and improve the traffic efficiency of an intersection.

With the development of society, users have increasing requirements for the high-quality experience of products. The pursuit of a high profit conversion rate also gradually puts forward higher requirements for product details in the competition. Product providers need to iterate products fast and with a high quality to enhance user viscosity and activity to improve the profit conversion rate efficiently. A/B testing is a technical method to conduct experiments on target users who use different iterative strategies, and observe which strategy is better through log embedding and statistical analysis. Usually, different businesses of the same company are supported by different business systems, and the A/B tests of different business systems need to be operated in a unified manner. At present, most A/B testing systems cannot provide services for more than one business system at the same time, and there are problems such as high concurrency, scalability, reusability, and flexibility. In this regard, this paper proposes an idea of dynamic strategy distribution, based on which a configuration-driven traffic-multiplexing A/B testing model is constructed and implemented systematically. The model solves the high-concurrency problem when requesting experimental strategies by setting message middleware and strategy cache modules, making the system more lightweight, flexible, and efficient to meet the A/B testing requirements for multiple business systems.

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