Link prediction in graphs with autoregressive features
Link prediction in graphs with autoregressive features
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DOI：
10.5555/2627435.2627453
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
E. Richard;Stéphane Gaïffas;N. Vayatis
中科院分区：
文献类型：
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作者：
E. Richard;Stéphane Gaïffas;N. Vayatis
本文研究了时间演化图中的链接预测问题。我们假设某些图形特征，如节点度，遵循向量自回归（VAR）模型，我们建议使用这些信息来提高预测的准确性。我们的策略涉及到一个联合优化过程的空间的邻接矩阵和VAR矩阵，同时考虑到稀疏性和低秩矩阵的属性。Oracle的不等式推导和说明的权衡，在平滑参数的选择建模稀疏性和低秩属性的联合效果时。通过一个广义的向前向后agorithm使用邻近的方法，有效地计算估计。
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.