Graph learning methods help utilize implicit relationships among data items, thereby reducing training label requirements and improving task performance. However, determining the optimal graph structure for a particular learning task remains a challenging research problem. In this work, we introduce the Graph Lottery Ticket (GLT) Hypothesis - that there is an extremely sparse backbone for every graph, and that graph learning algorithms attain comparable performance when trained on that subgraph as on the full graph. We identify and systematically study 8 key metrics of interest that directly influence the performance of graph learning algorithms. Subsequently, we define the notion of a "winning ticket" for graph structure - an extremely sparse subset of edges that can deliver a robust approximation of the entire graph's performance. We propose a straightforward and efficient algorithm for finding these GLTs in arbitrary graphs. Empirically, we observe that performance of different graph learning algorithms can be matched or even exceeded on graphs with the average degree as low as 5.

翻译：图学习方法有助于利用数据项间的隐式关联，从而降低训练标签需求并提升任务性能。然而，针对特定学习任务确定最优图结构仍是一个具有挑战性的研究问题。本文提出图彩票假说（Graph Lottery Ticket, GLT）——每张图均存在一个极稀疏的骨干结构，在此子图上训练的图学习算法可获得与全图训练相当的性能。我们识别并系统研究了直接影响图学习算法性能的8项关键度量指标，进而定义了图结构“中奖彩票”的概念：能够稳健逼近全图性能的极端稀疏边子集。我们提出了一种简洁高效的算法，可在任意图中发现这些GLT。实验表明，在平均度低至5的图上，不同图学习算法的性能可被匹配甚至超越。