论文标题
马尔可夫随机块模型中的可靠时间预测
Reliable Time Prediction in the Markov Stochastic Block Model
论文作者
论文摘要
我们介绍了Markov随机块模型（MSBM）：基于社区网络的增长模型，其中节点属性通过马尔可夫动态分配。我们依靠HMMS的文献来设计对局部聚类错误可靠的预测方法。我们专门关注链接预测和协作过滤问题，并引入了一个新的模型选择过程，以推断网络中隐藏的群集的数量。我们在MSBM中可靠预测的方法并非依赖性算法，因为它们可以使用您喜欢的聚类工具应用。 在本文中，我们使用最近的SDP方法来推断隐藏的社区，并提供理论保证。特别是，我们在框架中确定了相关的信号噪声比（SNR），并证明错误分类误差相对于此SNR呈指数速度衰减。
We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs' literature to design prediction methods that are robust to local clustering errors. We focus specifically on the link prediction and collaborative filtering problems and we introduce a new model selection procedure to infer the number of hidden clusters in the network. Our approaches for reliable prediction in MSBMs are not algorithm-dependent in the sense that they can be applied using your favourite clustering tool. In this paper, we use a recent SDP method to infer the hidden communities and we provide theoretical guarantees. In particular, we identify the relevant signal-to-noise ratio (SNR) in our framework and we prove that the misclassification error decays exponentially fast with respect to this SNR.
