标题： 带有自回归特征的图中的链接预测 显示英文标题

标题： Link Prediction in Graphs with Autoregressive Features

摘要： 在论文中，我们考虑时间演变图中的链接预测问题。 我们假设某些图特征，如节点度数，遵循向量自回归（VAR）模型，并提出利用此信息来提高预测的准确性。 我们的策略涉及对邻接矩阵和VAR矩阵空间的联合优化过程，该过程考虑了矩阵的稀疏性和低秩性质。 推导出Oracle不等式，并说明在建模稀疏性和低秩性质的联合效应时平滑参数选择的权衡。 通过广义前向后向算法，使用近端方法高效计算估计值。 显示英文摘要

摘要： In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we propose to use this information to improve the accuracy of prediction. Our strategy involves a joint optimization procedure over the space of adjacency matrices and VAR matrices which takes into account both sparsity and low rank properties of the matrices. Oracle inequalities are derived and illustrate the trade-offs in the choice of smoothing parameters when modeling the joint effect of sparsity and low rank property. The estimate is computed efficiently using proximal methods through a generalized forward-backward agorithm.