论文标题
专家：动态异构学术图的公共基准
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
论文作者
论文摘要
从动态图中学习的机器学习模型随着节点和边缘随时间而变化，在学习和推理方面面临着非平凡的挑战。社区广泛使用的现有大规模图基准数据集主要集中在均匀的节点和边缘属性上，并且是静态的。在这项工作中，我们介绍了各种大型，动态的异质学术图，以测试为多步图预测任务开发的模型的有效性。我们的新颖数据集涵盖了从两个社区的科学出版物中提取的上下文和内容信息：人工智能（AI）和核不增殖（NN）。此外，我们提出了一种系统的方法来改善图表预测模型中使用的现有评估程序。
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph benchmark datasets that are widely used by the community primarily focus on homogeneous node and edge attributes and are static. In this work, we present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for multi-step graph forecasting tasks. Our novel datasets cover both context and content information extracted from scientific publications across two communities: Artificial Intelligence (AI) and Nuclear Nonproliferation (NN). In addition, we propose a systematic approach to improve the existing evaluation procedures used in the graph forecasting models.
