GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection

arXiv:2404.01578v1 Announce Type: new
Abstract: The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting the right GL model becomes increasingly difficult and time consuming as more and more GL models are developed. Accordingly, it is of great significance and practical value to equip users of GL with the ability to perform a near-instantaneous selection of an effective GL model without manual …

arxiv benchmark cs.lg cs.si graph graph learning model selection type