DISL: Deep Isomorphic Substructure Learning for network representations

Abstract

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-F1 scores over the experimental datasets.