Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China.
Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences, China.
Neural Netw. 2022 Nov;155:74-83. doi: 10.1016/j.neunet.2022.07.035. Epub 2022 Aug 10.
Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classification due to their powerful capability in pattern learning. The varying numbers of neighbor nodes and the lack of canonical order of nodes on graphs pose challenges in constructing receptive fields for CNNs. Existing methods generally follow a heuristic ranking-based framework, which constructs receptive fields by selecting a fixed number of nodes and dropping the others according to predetermined rules. However, such methods may lose important structure information through dropping nodes, and they also cannot learn task-oriented graph patterns. In this paper, we propose a Location learning-based Convolutional Neural Networks (LCNN) for graph classification. LCNN constructs receptive fields by learning the location of each node according to its embedding that contains structures and features information, then standard CNNs are applied to capture graph patterns. Such a location learning mechanism not only retains the information of all nodes, but also provides the ability for task-oriented pattern learning. Experimental results show the effectiveness of the proposed LCNN, and visualization results further illustrate the valid pattern learning ability of our method for graph classification.
图模式在各种图分类任务中起着关键作用，例如，化学模式通常决定分子图的性质。由于卷积神经网络（CNN）在模式学习方面的强大能力，研究人员致力于将其应用于图分类。节点的邻居数量不同以及图上节点的缺乏规范顺序给 CNN 构建感受野带来了挑战。现有的方法通常遵循启发式排序的框架，该框架通过根据预定规则选择固定数量的节点并丢弃其他节点来构建感受野。然而，这种方法可能会通过丢弃节点而丢失重要的结构信息，并且也无法学习面向任务的图模式。在本文中，我们提出了一种用于图分类的基于位置学习的卷积神经网络（LCNN）。LCNN 通过根据其包含结构和特征信息的嵌入来学习每个节点的位置来构建感受野，然后应用标准的 CNN 来捕获图模式。这种位置学习机制不仅保留了所有节点的信息，还提供了面向任务的模式学习能力。实验结果表明了所提出的 LCNN 的有效性，可视化结果进一步说明了我们的方法在图分类方面的有效模式学习能力。
