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.
@article{arxiv.2204.07203,
title = {EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs},
author = {Sameera Horawalavithana and Ellyn Ayton and Anastasiya Usenko and Shivam Sharma and Jasmine Eshun and Robin Cosbey and Maria Glenski and Svitlana Volkova},
journal= {arXiv preprint arXiv:2204.07203},
year = {2022}
}