论文标题
使用具有多个较高阶的网络模型来预测图中遍历节点的序列
Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
论文作者
论文摘要
我们提出了一种新的序列预测方法，用于捕获图中的节点遍历的顺序数据。我们的方法建立在统计建模框架上，该统计建模框架将多个高阶网络模型结合到单个多阶模型中。我们开发了一种在经验顺序数据中拟合此类多阶模型的技术，并选择最佳的最大顺序。我们的框架促进了下一元素和完整序列预测，因为序列排名有任何长度。我们基于六个经验数据集评估模型，其中包含网站导航和公共交通系统的序列。结果表明，我们的方法超出了下一个元素预测的最先进算法。我们进一步证明了我们在样本外序列预测中方法的准确性，并验证我们的方法可以扩展到具有数百万个序列的数据集。
We propose a novel sequence prediction method for sequential data capturing node traversals in graphs. Our method builds on a statistical modelling framework that combines multiple higher-order network models into a single multi-order model. We develop a technique to fit such multi-order models in empirical sequential data and to select the optimal maximum order. Our framework facilitates both next-element and full sequence prediction given a sequence-prefix of any length. We evaluate our model based on six empirical data sets containing sequences from website navigation as well as public transport systems. The results show that our method out-performs state-of-the-art algorithms for next-element prediction. We further demonstrate the accuracy of our method during out-of-sample sequence prediction and validate that our method can scale to data sets with millions of sequences.
