基于时序图卷积的动态网络链路预测

动态网络链路预测广泛的应用前景，使得其逐渐成为网络科学研究的热点. 动态网络链路演化过程中具有复杂的空间相关性和时间依赖性，导致其链路预测任务极具挑战. 提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link prediction based on sequential graph convolution, DNLP-SGC). 针对网络快照序列不能有效反映动态网络连续性的问题，采用边缘触发机制对原始网络权重矩阵进行修正，弥补了离散快照表示动态网络存在时序信息丢失的不足. 从网络演化过程出发，综合考虑节点间的特征相似性以及历史交互信息，采用时序图卷积提取动态网络中节点的特征，该方法融合了节点时空依赖关系. 进一步，采用因果卷积网络捕获网络演化过程中潜在的全局时序特征，实现动态网络链路预测. 在2个真实的网络数据集上的实验结果表明，DNLP-SGC在precision, recall, AUC指标上均优于对比的基线模型.

Identity and search in social networks

Dynamic network link prediction has become a hot topic in network science field because of its wide application prospect. However, the complexity of spatial correlation and temporal dependence in the evolution process of dynamic network links leads to the great challenges of dynamic network link prediction task. In this paper, a dynamic network link prediction model based on sequential graph convolution (DNLP-SGC) is proposed. On the one hand, because network snapshot sequence cannot effectively reflect the continuity of dynamic network evolution, the edge trigger mechanism is employed to modify the original network weight matrix, so as to make up loss timing information in discrete snapshot of dynamic network. On the other hand, from the view of network evolution and considering the feature similarity and historical interaction information between nodes, a temporal graph convolution method is proposed to extract node features in dynamic network, and the method integrates the spatial-temporal dependence of nodes effectively. Furthermore, the causal convolutional network is used to capture the potential global temporal features in the dynamic network evolution process to achieve dynamic network link prediction. Experimental results on two real dynamic network datasets show that DNLP-SGC outperforms the baseline model on three common indexes, such as precision, recall and AUC.