PROSE: Graph Structure Learning via Progressive Strategy

Huizhao Wang, Yao Fu, Tao Yu, Linghui Hu, Weihao Jiang, Shiliang Pu

Abstract: Graph Neural Networks (GNNs) have been a powerful tool to acquire high-quality node representations dealing with graphs, which strongly depends on a promising graph structure. In the real world scenarios, it is inevitable to introduce noises in graph topology. To prevent GNNs from the disturbance of irrelevant edges or missing edges, graph structure learning is proposed and has attracted considerable attentions in recent years. In this paper, we argue that current graph structure learning methods still pay no regard to the status of nodes and just judge all of their connections simultaneously using a monotonous standard, which will lead to indeterminacy and instability in the optimization process. We designate these methods as status-unaware models. To demonstrate the rationality of our point of view, we conduct exploratory experiments on publicly available datasets, and discover some exciting observations. Afterwards, we propose a new model named Graph Structure Learning via Progressive Strategy (PROSE) according to the observations, which uses a progressive strategy to acquire ideal graph structure in a status-aware way. Concretely, PROSE consists of progressive structure splitting module (PSS) and progressive structure refining module (PSR) to modify node connections according to their global potency, and we also introduce horizontal position encoding and vertical position encoding in order to capture fruitful graph topology information ignored by previous methods. On several widely-used graph datasets, we conduct extensive experiments to demonstrate the effectiveness of our model, and the source code 1 https://github.com/tigerbunny2023/PROSE is provided.