Inspired by the conference tracks in the computer vision and natural language processing communities that are dedicated to establishing new benchmark datasets and tasks, we call for contributions that introduce novel ML tasks or novel graph-structured data which have the potential to (i) help understand the performance and limitations of graph representation models on diverse sets of problems and (ii) support benchmark evaluations for various models.

We especially (but not exclusively) call for submissions which will contribute to at least one of the following:

The acceptance of the contributed papers will be decided on the meaningfulness of the established graph learning tasks/datasets and their potential of being formalized into new benchmarks, rather than the performance of ML models (old or new) on these tasks. We particularly welcome contributions of negative results of popular, state-of-the-art models on a new task/dataset, as these provide novel insights to the community’s understanding of the meta-knowledge of graph ML.

Call for Papers

Important Dates


Abstracts and papers can be submitted through CMT: