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Graph neural network, as a deep learning based graph representation technology, can capture the structural information encapsulated in graphs well and generate more effective feature embedding. We have recently witnessed an emerging research interests on it. However, existing models are primarily focused on handling homogeneous graphs. When designing graph neural networks for heterogeneous graphs, heterogeneity and rich semantic information bring great challenges. In this paper, we extend graph neural network to heterogeneous graph scenes, and propose a novel high-order Symmetric Relation based Heterogeneous Graph Attention Network, denoted as SR-HGAT, which takes into account the features of nodes and high-order relations simultaneously, and exploits the two-layer attention mechanism based aggregator to efficiently capture essential semantics in an end-to-end manner. The proposed SR-HGAT first identifies the latent semantics underneath the observed explicit symmetric relations guided by different meta-paths and meta-graphs in a heterogeneous graph. The nested propagation mechanism for aggregating semantic and structural features that different links contain is then designed to calculate the interaction strength of each symmetric relation. As the core of the proposed model, to comprehensively capture both the structural and semantic feature information, a two-layer attention mechanism is applied to learn the importance of different neighborhood information as well as the weights of different symmetric relations. These latent semantics are then automatically fused to obtain unified embeddings for specific mining tasks. Extensive experimental results offer insights into the efficacy of the proposed model and have demonstrated that it significantly outperforms state-of-the-art baselines across three benchmark datasets on various downstream tasks.

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

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