Attribute Network Representation Learning with Dual Autoencoders

Abstract

The purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. The current network representation learning methods have insufficient interaction with structure when learning attribute information, and the structure and attribute information cannot be well integrated. In this paper, we propose an attribute network representation learning method for dual-channel autoencoder. One channel is for the network structure, and adopting the multi-hop attention mechanism is used to capture the node’s high-order neighborhood information and calculate the neighborhood weight; The other channel is for the node attribute information, and a low-pass Laplace filter is designed to iteratively obtain the attribute information in the neighborhood of the node. The dual-channel autoencoder ensures the learning of structure and attribute information respectively. The adaptive fusion module is constructed in this method to increase the acquisition of important information through the consistency and difference constraints of two kinds of information. The method trains encoders by supervising the joint reconstruction of loss functions of two autoencoders. Based on the node clustering task on four authentic open data sets, and compared with eight network representation learning algorithms in clustering accuracy, standardized mutual information and running time of some algorithms, the experimental results show that the proposed method is superior and reasonable.