Position-Aware Network Representation Learning via K-Step Mutual Information Estimation

As the network data grows rapidly and persistently, also affiliated with more sophisticated applications, the network representation learning, which aims to learn the high-quality embedding vectors, has become the popular methodology to perform various network analysis tasks. However, the existing representation learning methods have little power in capturing the positional/locational information of the node. To handle the problem, this paper proposes a novel position-aware network representation learning model by figuring out center-rings mutual information estimation to plant the node’s global position into the embedding, PMI for short. The proposed PMI encourages each node to respectively perceive its K-step neighbors via the maximization of mutual information between this node and its step-specific neighbors. The extensive experiments using four real-world datasets on several representative tasks demonstrate that PMI can learn high-quality embeddings and achieve the best performance compared with other state-of-the-art models. Furthermore, a novel neighbor alignment experiment is additionally provided to verify that the learned embedding can identify its K-step neighbors and capture the positional information indeed to generate appropriate embeddings for various downstream tasks.