Completing a graph means inferring the missing nodes and edges from a partially observed network. Different methods have been proposed to solve this problem, but none of them employed the pattern similarity of parts of the graph. In this paper, we propose a model to use the learned pattern of connections from the observed part of the network based on the Graph Auto-Encoder technique and generalize these patterns to complete the whole graph. Our proposed model achieved competitive performance with less information needed. Empirical analysis of synthetic datasets and real-world datasets from different domains show that our model can complete the network with higher accuracy compared with baseline prediction models in most cases. Furthermore, we also studied the character of the model and found it is particularly suitable to complete a network that has more complex local connection patterns.

该研究利用图自编码器框架中的图同构网络模型，通过捕捉网络的局部结构模式来完成整个网络的建模，同时提出了一种基于网络结构的评估指标“可达聚类系数”，实验证明该模型在需要较少信息的情况下能够达到竞争性的性能表现，并在大多数情况下比基线预测模型更精确地完成网络的建模。

学习本地连接模式完成网络