Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the role-similar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features fusion (REQF), which simultaneously considers the influence of global and local role information, node features, and noise. Firstly, we capture the global role information of nodes via quantum walk based on its superposition property which emphasizes the local role information via biased quantum walk. Secondly, we utilize the quantum walk weighted characteristic function to extract and fuse features of nodes and their neighborhood by different distributions which contain role information implicitly. Finally, we leverage the Variational Auto-Encoder (VAE) to reduce the effect of noise. We conduct extensive experiments on seven real-world datasets, and the results show that REQF is more effective at capturing role information in the network, which outperforms the best baseline by up to 14.6% in role classification, and 23% in role detection on average.