论文标题
链接预测：图形模型方法
Link Prediction: A Graphical Model Approach
论文作者
论文摘要
我们考虑了网络中的链接预测问题，其边缘结构可能会随着时间的流逝而变化（足够缓慢）。这个问题在包括社交网络在内的许多重要领域的应用程序都有两个主要变体：第一个（称为积极的链接预测或PLP）在估计网络中链接的外观。第二个称为负链路预测或NLP的链接在于估计网络中链接的消失。我们提出了一种数据驱动的方法来估计边缘的外观/消失。我们的解决方案基于一个正规化的优化问题，我们证明了最佳解决方案的存在和独特性。
We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as negative link prediction or NLP consists in estimating the disappearance of a link in the network. We propose a data-driven approach to estimate the appearance/disappearance of edges. Our solution is based on a regularized optimization problem for which we prove existence and uniqueness of the optimal solution.
