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.

本研究针对机器学习模型在动态图中的学习和推理遇到的挑战，针对传统静态同构图数据集的局限性，提出了一种利用多元素科学出版涵盖的动态异构学术图数据集，测试模型预测任务的效能，并提出了一种系统方法来改善现有的图预测模型评估程序。