With the rapid proliferation of scientific literature, versatile academic
knowledge services increasingly rely on comprehensive academic graph mining.
Despite the availability of public academic graphs, benchmarks, and datasets,
these resources often fall short in multi-aspect and fine-grained annotations,
are constrained to specific task types and domains, or lack underlying real
academic graphs. In this paper, we present OAG-Bench, a comprehensive,
multi-aspect, and fine-grained human-curated benchmark based on the Open
Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines,
and 120+ experimental results to date. We propose new data annotation
strategies for certain tasks and offer a suite of data pre-processing codes,
algorithm implementations, and standardized evaluation protocols to facilitate
academic graph mining. Extensive experiments reveal that even advanced
algorithms like large language models (LLMs) encounter difficulties in
addressing key challenges in certain tasks, such as paper source tracing and
scholar profiling. We also introduce the Open Academic Graph Challenge
(OAG-Challenge) to encourage community input and sharing. We envisage that
OAG-Bench can serve as a common ground for the community to evaluate and
compare algorithms in academic graph mining, thereby accelerating algorithm
development and advancement in this field. OAG-Bench is accessible at
this https URL

本文介绍了基于开放学术图的全面、多方面和细粒度的人工筛选基准 OAG-Bench，涵盖了 10 个任务、20 个数据集、70 + 基线和 120 + 实验结果，并提供了新的数据注释策略、数据预处理代码、算法实现和标准化评估协议，以促进学术图挖掘的发展。