In recent years, Graph Neural Networks (GNNs) have facilitated the development of graph data mining. However, training GNNs requires sufficient labeled task-specific data, which is expensive and sometimes unavailable. To be less dependent on labeled data, recent studies propose to pre-train GNNs in a self-supervised manner and then apply the pre-trained GNNs to downstream tasks with limited labeled data. However, most existing methods are designed solely for homogeneous graphs (real-world graphs are mostly heterogeneous) and do not consider semantic mismatch (the semantic difference between the original data and the ideal data containing more transferable semantic information). In this article, we propose an effective framework to pre-train GNNs on the large-scale heterogeneous graph. We first design a structure-aware pre-training task, which aims to capture structural properties in heterogeneous graphs. Then, we design a semantic-aware pre-training task to tackle the mismatch. Specifically, we construct a perturbation subspace composed of semantic neighbors to help deal with the semantic mismatch. Semantic neighbors make the model focus more on the general knowledge in the semantic space, which in turn assists the model in learning knowledge with better transferability. Finally, extensive experiments are conducted on real-world large-scale heterogeneous graphs to demonstrate the superiority of the proposed method over state-of-the-art baselines. Code available at https://github.com/sunshy-1/PHE .

User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-quality data from low-quality data. However, they tend to employ blackbox design, lacking transparency and interpretability. Besides, they are typically tailored to specific evaluation metrics, leading to limited generality across various tasks. To overcome these issues, we propose an explainable and versatile framework DVR which can enhance the efficiency of data utilization tailored to any requirements of the model architectures and evaluation metrics. For explainable data valuation, a data valuator is presented to evaluate the data quality via calculating its Shapley value from the game-theoretic perspective, ensuring robust mathematical properties and reliability. In order to accommodate various evaluation metrics, including differentiable and non-differentiable ones, a metric adapter is devised based on reinforcement learning, where a metric is treated as the reinforcement reward that guides model optimization. Extensive experiments conducted on various benchmarks verify that our framework can improve the performance of current recommendation algorithms on various metrics including ranking accuracy, diversity, and fairness. Specifically, our framework achieves up to 34.7% improvements over existing methods in terms of representative NDCG metric. The code is available at https://github.com/renqii/DVR.

The generation of medical dialogue notes is essential in healthcare, providing a structured recapitalization of patient-provider interactions. Medical notes are rigorously organized into various sections, including Chief Complaint, History of Present Illness and more. Each section serves a specific purpose to record detailed medical content. Traditionally, this task is labor-intensive, requiring physicians to manually create notes, a process prone to errors. With advancements in AI, it is now feasible to automate the generation of medical notes. There are mainly two categories of methods for automatic medical note generation. Pre-trained language models (PLMs) struggle with unstructured outputs, limited datasets, and inadequate medical terminology. In-context learning (ICL) methods improve accuracy and reduce data requirements but still produce unstructured notes and require high time and cost. To tackle the above challenges, we propose a three-module framework, called CE-DEPT, for accurate, efficient and cost-effective medical note generation. Specifically, the Task Decomposition Module breaks down complete medical dialogues into section-specific dialogues to ensure relevance and accuracy. The Batch Combination Module groups these sections into batches based on disease similarity to reduce costs and improve efficiency. The Note Generation Module employs batch prompting with ICL to generate each section note, followed by combining them into a structured, comprehensive medical note. Experiments on benchmark datasets demonstrated the effectiveness of Task Decomposition and Batch Prompting. Our method, CE-DEPT outperforms the best method by 5% on the ROUGE-1 score, 3% on the Bertscore-F1, a cost-effectiveness improvement of 15%, and a reduction in time consumption of 25% at peak accuracy.