The analysis of complex networks based on deep learning has drawn much attention recently. Generally, due to the scale and complexity of modern networks, traditional methods are gradually losing the analytic efficiency and effectiveness. Therefore, it is imperative to design a network analysis model which caters to the massive amount of data and learns more comprehensive information from networks. In this paper, we propose a novel model, namely Deep Isomorphic Substructure Learning (DISL) model, which aims to learn network representations from patterns with isomorphic substructures. Specifically, in DISL, deep learning techniques are used to learn a better network representation for each vertex (node). We provide the method that makes the isomorphic units self-embed into vertex-based subgraphs whose explicit topologies are extracted from raw graph-structured data, and design a Probability-guided Random Walk (PRW) procedure to explore the set of substructures. Sequential samples yielded by PRW provide the information of relational similarity, which integrates the information of correlation and co-occurrence of vertices and the information of substructural isomorphism of subgraphs. We maximize the likelihood of the preserved relationships for learning the implicit similarity knowledge. The architecture of the Convolutional Neural Networks (CNNs) is redesigned for simultaneously processing the explicit and implicit features to learn a more comprehensive representation for networks. The DISL model is applied to several vertex classification tasks for social networks. Our results show that DISL outperforms the challenging state-of-the-art Network Representation Learning (NRL) baselines by a significant margin on accuracy and weighted-$F_1$ scores over the experimental datasets.

基于深度学习的复杂网络分析近来备受关注。通常，由于现代网络的规模和复杂性，传统方法逐渐失去了分析效率和有效性。因此，必须设计一种网络分析模型以迎合海量数据并从网络中学习更全面的信息。在本文中，我们提出了一个新颖的模型，即深度同构子结构学习（DISL）模型，旨在从具有同构子结构的模式中学习网络表示。具体而言，在DISL中，深度学习技术用于为每个顶点（节点）学习更好的网络表示。我们提供了使同构单元自嵌入基于顶点的子图中的方法，这些子图的显式拓扑结构是从原始图结构数据中提取的，并设计了概率指导的随机游走（PRW）程序来探索子结构集。PRW产生的顺序样本提供了关系相似性信息，该信息整合了顶点的相关性和共现信息以及子图的子结构同构信息。我们最大程度地保留了用于学习隐式相似性知识的关系。卷积神经网络（CNN）的架构经过重新设计，可以同时处理显式和隐式特征，以学习网络的更全面表示。DISL模型应用于社交网络的多个顶点分类任务。我们的结果表明，DISL在准确性和加权权重方面有显着优势，胜过了具有挑战性的最新网络表示学习（NRL）基准。$F_{\mathrm{1个}}$ 得分超过实验数据集。