IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification
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发表时间：
2019-07
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通讯作者：
Lin Meng;Jiawei Zhang
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作者：
Lin Meng;Jiawei Zhang
深度学习模型在许多领域取得了巨大的成功，例如计算机视觉和自然语言处理。然而，与这些领域不同的是，由于“节点无序”的特性，很难将传统的深度学习模型应用于图数据。通常情况下，邻接矩阵会在图上投射一个人为的和随机的节点顺序，这使得深度模型在图分类任务上的性能非常不稳定，并且这些模型学习的表示缺乏清晰的可解释性。为了消除不必要的节点顺序约束，我们提出了一种新的模型命名为同构神经网络（IsoNN），它通过输入图和模板之间的图匹配提取其同构特征来学习图表示。IsoNN有两个主要组件：图同构特征提取组件和分类组件。图同构特征提取组件利用一组子图模板作为核变量来学习输入图中可能存在的子图模式，然后计算同构特征。在组件中使用一组置换矩阵来打破由矩阵表示带来的节点顺序。三个全连接层被用作IsoNN中的分类组件。在基准数据集上进行了大量的实验，实验结果表明ISONN的有效性，特别是与经典和最先进的图分类方法相比。
Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property. Normally, adjacency matrices will cast an artificial and random node-order on the graphs, which renders the performance of deep models on graph classification tasks extremely erratic, and the representations learned by such models lack clear interpretability. To eliminate the unnecessary node-order constraint, we propose a novel model named Isomorphic Neural Network (IsoNN), which learns the graph representation by extracting its isomorphic features via the graph matching between input graph and templates. IsoNN has two main components: graph isomorphic feature extraction component and classification component. The graph isomorphic feature extraction component utilizes a set of subgraph templates as the kernel variables to learn the possible subgraph patterns existing in the input graph and then computes the isomorphic features. A set of permutation matrices is used in the component to break the node-order brought by the matrix representation. Three fully-connected layers are used as the classification component in IsoNN. Extensive experiments are conducted on benchmark datasets, the experimental results can demonstrate the effectiveness of ISONN, especially compared with both classic and state-of-the-art graph classification methods.