Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder

Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications. Attributed network embedding aims to learn the low-dimensional representation of network nodes by integrating network architecture and attribute information. The network architectures of many real-world applications are complex, and the relations between network architectures and their attributed nodes are opaque. Thus, shallow models fail to capture deep nonlinear information when an attributed network is embedded, leading to unreliable embedding. In the present paper, a Deep Attributed Network Embedding via Weisfeiler-Lehman and Autoencoder (DANE-WLA) is proposed in order to capture high nonlinearity and preserve the many proximities in the network attribute information of nodes and structures. Weisfeiler-Lehman proximity schema was used to capture the node dependency between both node edges and node attributes based on information sequences. Then, a deep autoencoder was applied to invest complex nonlinear information. Extensive experiments were conducted on benchmark datasets to verify that DANE-WLA is computationally efficient for various tasks requiring network embedding. The experimental results show that our model outperforms the state-of-the-art network embedding models.