Deep attributed network representation learning via attribute enhanced neighborhood

Attributed network representation learning aims at learning node embeddings by integrating network structure and attribute information. It is a challenge to fully capture the microscopic structure and the attribute semantics simultaneously, where the microscopic structure includes the one-step, two-step and multi-step relations, indicating the first-order, second-order and high-order proximity of nodes, respectively. In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations. The DANRL-ANE model adopts the idea of the autoencoder, and expands the decoder component to three branches to capture different order proximity. We linearly combine the adjacency matrix with the attribute similarity matrix as the input of DANRL-ANE, where the attribute similarity matrix is calculated by the cosine similarity between the attributes based on the social homophily. Moreover, the sigmoid cross-entropy loss function is extended to capture the neighborhood character, so that the first-order proximity could be well-preserved. We compare our model with the state-of-the-art models, especially, the latest graph autoencoders (GAEs) method, ARGA, and demonstrate the contribution of each module on real-world datasets and two network analysis tasks, i.e., link prediction and node classification. The DANRL-ANE model performs well on various networks, even on sparse networks or networks with isolated nodes when the attribute information is sufficient. (C) 2022 Published by Elsevier B.V.