  Graph neural networks (GNNs) have been widely used in many graph-based tasks
such as node classification, link prediction, and node clustering. However,
GNNs gain their performance benefits mainly from performing the feature
propagation and smoothing across the edges of the graph, thus requiring
sufficient connectivity and label information for effective propagation.
Unfortunately, many real-world networks are sparse in terms of both edges and
labels, leading to sub-optimal performance of GNNs. Recent interest in this
sparse problem has focused on the self-training approach, which expands
supervised signals with pseudo labels. Nevertheless, the self-training approach
inherently cannot realize the full potential of refining the learning
performance on sparse graphs due to the unsatisfactory quality and quantity of
pseudo labels.
  In this paper, we propose ROD, a novel reception-aware online knowledge
distillation approach for sparse graph learning. We design three supervision
signals for ROD: multi-scale reception-aware graph knowledge, task-based
supervision, and rich distilled knowledge, allowing online knowledge transfer
in a peer-teaching manner. To extract knowledge concealed in the multi-scale
reception fields, ROD explicitly requires individual student models to preserve
different levels of locality information. For a given task, each student would
predict based on its reception-scale knowledge, while simultaneously a strong
teacher is established on-the-fly by combining multi-scale knowledge. Our
approach has been extensively evaluated on 9 datasets and a variety of
graph-based tasks, including node classification, link prediction, and node
clustering. The result demonstrates that ROD achieves state-of-art performance
and is more robust for the graph sparsity.
