论文标题
在Bernoulli自动收入框架上，用于链接发现和预测
On a Bernoulli Autoregression Framework for Link Discovery and Prediction
论文作者
论文摘要
我们为二进制序列提供了一个动态预测框架，该序列基于自动回归过程的Bernoulli概括。我们的方法很容易适合一系列依赖时间的网络的标准链路预测问题的变体。着眼于这个动态网络链接预测/推荐任务，我们提出了一个新的问题，该问题通过更大的辅助网络序列来利用其他信息，并且具有重要的现实世界相关性。为了发现可用数据中不存在的链接，我们的模型估计框架引入了一个正规化术语，该术语在常规链接预测和此发现任务之间进行了权衡。与现有工作相反，我们基于随机梯度的估计方法非常有效，并且可以扩展到具有数百万节点的网络。我们对基于实际产品的时间依赖性网络都显示了广泛的经验结果，并在基于REDDIT的时间依赖性情感序列的基于REDDIT的数据集上呈现结果。
We present a dynamic prediction framework for binary sequences that is based on a Bernoulli generalization of the auto-regressive process. Our approach lends itself easily to variants of the standard link prediction problem for a sequence of time dependent networks. Focusing on this dynamic network link prediction/recommendation task, we propose a novel problem that exploits additional information via a much larger sequence of auxiliary networks and has important real-world relevance. To allow discovery of links that do not exist in the available data, our model estimation framework introduces a regularization term that presents a trade-off between the conventional link prediction and this discovery task. In contrast to existing work our stochastic gradient based estimation approach is highly efficient and can scale to networks with millions of nodes. We show extensive empirical results on both actual product-usage based time dependent networks and also present results on a Reddit based data set of time dependent sentiment sequences.
