The rapid growth of online shopping has intensified competition among logistics companies, highlighting the importance of customer expansion, i.e., identifying customers willing to establish long-term contracts. Although existing approaches frame customer expansion as a node classification task using heterogeneous graph learning to capture complex interactions between a customer and other items, it is computationally infeasible to utilize all neighboring interactions on large-scale logistics graphs. Current sub-sampling methods reduce computational load by sampling a small part of neighborhood for training. However, they introduce substantial information loss, particularly affecting high-degree nodes and decreasing predictive accuracy. To address this, we introduce NeighSqueeze, a novel approach that groups structurally and semantically similar nodes, substantially reducing the neighbors count and facilitating full-neighbor learning. NeighSqueeze consists of three modules designed to efficiently and effectively enable node grouping on billion-scale heterogeneous graphs: (1) Structure-tightness-based neighbor filtering reduces the high redundancy and complexity in similarity computations. (2) Hybrid similarity graph construction addresses the difficulty of measuring node similarity at scale; and (3) A two-level grouping strategy resolves the label dominance issue within groups. We evaluate NeighSqueeze on JD Logistics, one of the largest logistics companies in China. Compared with sub-sampling methods, our NeighSqueeze exhibits lower runtime and memory usage with full-neighbor training on the compressed graph, while simultaneously improving average precision over 28.9% in offline evaluation and increase new customer exploration rate by 18.6% in online A/B testing.

Customer expansion, i.e., growing a business's existing customer base by acquiring new customers, is critical for scaling operations and sustaining the long-term profitability of logistics companies. Although state-of-the-art works model this task as a single-node classification problem under a heterogeneous graph learning framework and achieve good performance, they struggle with extremely positive label sparsity issues in our scenario. Multi-task learning (MTL) offers a promising solution by introducing a correlated, label-rich task to enhance the label-sparse task prediction through knowledge sharing. However, existing MTL methods result in performance degradation because they fail to discriminate task-shared and task-specific structural patterns across tasks. This issue arises from their limited consideration of the inherently complex structure learning process of heterogeneous graph neural networks, which involves the multi-layer aggregation of multi-type relations. To address the challenge, we propose a Structure-Aware Hierarchical Information Sharing Framework (SrucHIS), which explicitly regulates structural information sharing across tasks in logistics customer expansion. SrucHIS breaks down the structure learning phase into multiple stages and introduces sharing mechanisms at each stage, effectively mitigating the influence of task-specific structural patterns during each stage. We evaluate StrucHIS on both private and public datasets, achieving a 51.41% average precision improvement on the private dataset and a 10.52% macro F1 gain on the public dataset. StrucHIS is further deployed at one of the largest logistics companies in China and demonstrates a 41.67% improvement in the success contract-signing rate over existing strategies, generating over 453K new orders within just two months.

Traffic accident prediction is a crucial problem for public safety, emergency treatment, and urban management. Existing works leverage extensive data collected from city infrastructures to achieve encouraging performance based on various machine learning techniques but cannot achieve a good performance in situations with limited data (i.e., data scarcity). Recent developments in transfer learning bring a new opportunity to solve the data scarcity problem. In this paper, we design a novel cross-city transfer learning framework named CARPG for predicting traffic accidents in data-scarce cities. We address the unique challenge of predicting traffic accidents caused by its two fundamental characteristics, i.e., spatial heterogeneity and inherent rareness, which result in the biased performance of the state-of-the-art transfer learning methods. Specifically, we build cross-city region connections by jointly learning the spatial region representations for both source and target cities with an inter-city global graph knowledge transfer process. Further, we design an efficient attention-based parameter-generating mechanism to learn region-specific traffic accident patterns, while controlling the total number of parameters. Built upon that, we ensure that only relevant patterns are transferred to each target region during the knowledge transfer process and further to be fine-tuned. We conduct extensive experiments on three real-world datasets, and the evaluation results demonstrate the superiority of our framework compared with state-of-the-art baseline models.