ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (8): 1612-1623.doi: 10.7544/issn1000-1239.2021.20210321

Special Issue: 2021人工智能前沿进展专题

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

Chu Xiaokai1,2, Fan Xinxin2, Bi Jingping2   

  1. 1(University of Chinese Academy of Sciences, Beijing 100049);2(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)
  • Online:2021-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62077044,61702470, 62002343).

Abstract: 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.

Key words: network representation learning, mutual information estimation, node representation, information network analysis, neural networks, node classification, link prediction

CLC Number: