Node embedding with capsule generation-embedding network

Achieving interpretable embedding of real network has a significant impact on network analysis tasks. However, majority of node embedding-based methods seldom consider the rationality and interpretability of node embedding. Although graph attention networks-based approaches have been employed to improve the interpretability of node embedding, they are implicitly specifying different weights to different nodes in a neighborhood. In this study, we present node embedding with capsule generation-embedding network(CapsGE), which is a novel capsule network-based network architecture, and uses node density based on the definition of uncertainty of node community belongings to explicitly assign different weights to different nodes in a neighborhood. In addition, this model uses the proposed cognitive reasoning mechanism for the weighted features to achieve rational and interpretable embedding of nodes. The performance of the method is assessed on node classification task. The experimental results demonstrate its advantages over other methods.