  Complex networks are widely used to represent an abundance of real-world
relations ranging from social networks to brain networks. Inferring missing
links or predicting future ones based on the currently observed network is
known as the link prediction task.Recent network embedding based link
prediction algorithms have demonstrated ground-breaking performance on link
prediction accuracy. Those algorithms usually apply node attributes as the
initial feature input to accelerate the convergence speed during the training
process. However, they do not take full advantage of node feature information.
In this paper,besides applying feature attributes as the initial input, we make
better utilization of node attribute information by building attributable
networks and plugging attributable networks into some typical link prediction
algorithms and naming this algorithm Attributive Graph Enhanced Embedding
(AGEE). AGEE is able to automatically learn the weighting trades-off between
the structure and the attributive networks. Numerical experiments show that
AGEE can improve the link prediction accuracy by around 3% compared with link
prediction framework SEAL, Variational Graph AutoEncoder (VGAE), and Node2vec.
