Dual Attention-Based Adversarial Autoencoder for Attributed Network Embedding

Existing embedding methods for Attributed Network aim to learn low-dimensional embeddings for nodes, which can preserve both consistency and complementarity for network structures and node attributes. The main assumption is that nodes with similar structures and/or similar attributes should be close in the embedding space. In reality, nodes with similar attributes might be far away from each other in topology and vice versa. The conflict is often caused by noisy links or incomplete network structures. Previous methods either independently project embeddings based on the assumption without considering the conflicts, or encode embeddings into a shared space ignoring the complementarity. In this paper, we propose a Dual Attention-based Adversarial Attributed Network Embedding framework (DAANE) to preserve the consistency and complementarity between structures and attributes, and reduce the conflict caused by their discrepancy. DAANE includes an attribute attention mechanism designed to detect and weakening the impact of noisy links and a structure attention mechanism applied to assign weights to network structures of different scales and capture a more complete global context. Furthermore, we develop efficient adversarial learning when combining the two heterogeneous embeddings. The adversarial auto-encoder projects embeddings of attributes and structures into the same space. Meanwhile, it completely circumvents the interference of various types of noise by removing the constraints of embedding space. Extensive experiments on three real-world network datasets indicate that the proposed model achieves state-of-the-art results.