Graph Structure Learning via Lottery Hypothesis at Scale

Graph Neural Networks (GNNs) are commonly applied to analyze real-world graph structured data. However, GNNs are sensitive to the given graph structure, which cast importance on graph structure learning to find optimal graph structures and representations. Previous methods have been restricted from large graphs due to high computational complexity. Lottery ticket hypothesis suggests that there exists a subnetwork that has comparable or better performance with proto-networks, which has been transferred to suit for pruning GNNs recently. There are few studies that address lottery ticket hypothesis's performance on defense in graphs. In this paper, we propose a scalable graph structure learning method leveraging lottery (ticket) hypothesis : GSL-LH. Our experiments show that GSL-LH can outperform its backbone model without attack and show better robustness against attack, achieving state-of-the-art performances in regular-size graphs compared to other graph structure learning methods without feature augmentation. In large graphs, GSL-LH can have comparable results with state-of-the-art defense methods other than graph structure learning, while bringing some insights into explanation of robustness.(1) (2) (3)