Link prediction in complex networks is to discover hidden or to-be-generated links between network nodes. Most of the mainstream graph neural network (GNN) based link prediction methods mainly focus on the representation learning of nodes, and are prone to over-smoothing problem. This paper dedicates to the representation learning of links, and designs an edge convolution operation so as to realize the link representation learning. Besides, we propose an normalization strategy for the learned link representation, for the purpose of alleviating the over-smoothing problem of edge convolution based link prediction model, when constructing the link prediction graph neural network EdgeConvNorm with stacking edge convolution manipulations. Lastly, we employ a binary classifier sigmod on the Hadamard product of two nodes representation parsed from the final learned link representation. The EdgeConvNorm can also be employed as a baseline, and extensive experiments on real-world benchmark complex networks validate that EdgeConvNorm not only alleviates the over-smoothing problem, but also has advantages over representative baselines.

复杂网络中的链路预测是发现网络节点之间隐藏的或将要生成的链路。大多数主流的基于图神经网络（GNN）的链接预测方法主要集中在节点的表示学习上，容易出现过平滑问题。本文致力于链路的表征学习，并设计了边缘卷积运算以实现链路表征学习。此外，我们提出了一种用于学习的链接表示的归一化策略，目的是在构建具有堆叠边缘卷积操作的链接预测图神经网络EdgeConvNorm时，缓解基于边缘卷积的链接预测模型的过度平滑问题。最后，我们采用二元分类器sigmod在从最终学习到的链接表示中解析出的两个节点表示的 Hadamard 积上。该EdgeConvNorm也可以作为基线，并在真实世界的标杆复杂网络大量的实验验证EdgeConvNorm不仅减轻了过平滑的问题，而且还拥有代表基线优势。