Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecules and social networks. They typically learn representations by aggregating information from the $K$-hop neighborhood of individual vertices or from the enumerated walks in the graph. Prior studies have demonstrated the effectiveness of incorporating weighting schemes into GNNs; however, this has been primarily limited to $K$-hop neighborhood GNNs so far. In this paper, we aim to design an algorithm incorporating weighting schemes into walk-aggregating GNNs and analyze their effect. We propose a novel GNN model, called AWARE, that aggregates information about the walks in the graph using attention schemes. This leads to an end-to-end supervised learning method for graph-level prediction tasks in the standard setting where the input is the adjacency and vertex information of a graph, and the output is a predicted label for the graph. We then perform theoretical, empirical, and interpretability analyses of AWARE. Our theoretical analysis in a simplified setting identifies successful conditions for provable guarantees, demonstrating how the graph information is encoded in the representation, and how the weighting schemes in AWARE affect the representation and learning performance. Our experiments demonstrate the strong performance of AWARE in graph-level prediction tasks in the standard setting in the domains of molecular property prediction and social networks. Lastly, our interpretation study illustrates that AWARE can successfully capture the important substructures of the input graph. The code is available on $\href{https://github.com/mehmetfdemirel/aware}{GitHub}$.
翻译:显示其具有强大的代表力,可用于在分子和社会网络等图表结构数据中进行下游预测任务,例如分子和社会网络。它们通常通过汇总个人脊椎周围的K$-hop信息或图表中列举的行走来学习表示力。先前的研究显示,将加权办法纳入GNNS是有效的;然而,迄今为止,这主要限于$-k$-hop邻居GNNS。在本文中,我们的目标是设计一种算法,将加权办法纳入步行聚合GNNS并分析其效果。我们提议了一个叫AWARRE的新型GNN图形模型模型,该模型利用注意办法将关于图表中行走的信息汇总起来。这导致在标准环境中,将加权办法纳入GPNS的加权办法;但是,这主要局限于一个图表的相近和顶点信息,而产出是该图的预测标签。我们随后对AWARE进行理论、实证和可解释性能分析。我们在一个简化的简化的设置中确定重要条件的GNNNNRE 数据库中,展示了我们高级的数学模型模型模型的模拟结构,展示了我们进行如何演化的深度分析。