DiffusAL: Coupling Active Learning with Graph Diffusion for
  Label-Efficient Node Classification
Sandra Gilhuber Julian Busch Daniel Rotthues Christian M. M. Frey Thomas Seidl
Node classification is one of the core tasks on attributed graphs, but
successful graph learning solutions require sufficiently labeled data. To keep
annotation costs low, active graph learning focuses on selecting the most
qualitative subset of nodes that maximizes label efficiency. However, deciding
which heuristic is best suited for an unlabeled graph to increase label
efficiency is a persistent challenge. Existing solutions either neglect
aligning the learned model and the sampling method or focus only on limited
selection aspects. They are thus sometimes worse or only equally good as random
sampling. In this work, we introduce a novel active graph learning approach
called DiffusAL, showing significant robustness in diverse settings. Toward
better transferability between different graph structures, we combine three
independent scoring functions to identify the most informative node samples for
labeling in a parameter-free way: i) Model Uncertainty, ii) Diversity
Component, and iii) Node Importance computed via graph diffusion heuristics.
Most of our calculations for acquisition and training can be pre-processed,
making DiffusAL more efficient compared to approaches combining diverse
selection criteria and similarly fast as simpler heuristics. Our experiments on
various benchmark datasets show that, unlike previous methods, our approach
significantly outperforms random selection in 100% of all datasets and labeling
budgets tested.