EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs

• URL: http://arxiv.org/abs/2204.07203v1
• Date: Thu, 14 Apr 2022 19:43:34 GMT
• Title: EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
• Authors: Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko, Shivam Sharma, Jasmine Eshun, Robin Cosbey, Maria Glenski, and Svitlana Volkova
• Abstract summary: We present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for 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)
• Score: 5.4744970832051445
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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