Pascal Plettenberg, Denis Huseljic, André Alcalde, Bernhard Sick, and Josephine M. Thomas present a new conference article entitled “ALINC: Active Learning for Inductive Node Classification via Graph Sampling” at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2026. The paper introduces ALINC, an active learning framework that selects entire graphs for annotation in inductive node classification. Application examples include site-of-metabolism prediction in molecules and design automation for printed circuit board schematics.

The article has been published by Springer and is available at https://link.springer.com/chapter/10.1007/978-3-032-37654-1_6. The accompanying code is available on GitHub at https://github.com/pasplett/alinc.