Curriculum Learning for Graph Neural Networks: A Multiview
  Competence-based Approach
Nidhi Vakil Hadi Amiri
A curriculum is a planned sequence of learning materials and an effective one
can make learning efficient and effective for both humans and machines. Recent
studies developed effective data-driven curriculum learning approaches for
training graph neural networks in language applications. However, existing
curriculum learning approaches often employ a single criterion of difficulty in
their training paradigms. In this paper, we propose a new perspective on
curriculum learning by introducing a novel approach that builds on graph
complexity formalisms (as difficulty criteria) and model competence during
training. The model consists of a scheduling scheme which derives effective
curricula by accounting for different views of sample difficulty and model
competence during training. The proposed solution advances existing research in
curriculum learning for graph neural networks with the ability to incorporate a
fine-grained spectrum of graph difficulty criteria in their training paradigms.
Experimental results on real-world link prediction and node classification
tasks illustrate the effectiveness of the proposed approach.