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本地分布式多网络社区检测的隐私保护迁移学习AI 解读

Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves community detection on a \emph{target network} by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary \emph{source networks}. Our focus is the \textit{local differential privacy} regime, in which each local data provider…

通过随机梯度下降对情境老虎机进行在线统计推断AI 解读

With the fast development of big data, learning the optimal decision rule by recursively updating it and making online decisions has been easier than before. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for an online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes…

大规模社交网络中的门户节点筛选AI 解读

Network autoregression model (NAM), as a powerful tool to study user social behaviors on large scale social networks, has drawn great attention in recent years. In this paper, we are interested in identifying the influential users (i.e., portal nodes) in a social network under the framework of NAM. Especially, we consider the autoregression model that allows to have a heterogeneous and sparse network effect coefficients. Therefore, the portal nodes take influential powers…

随机分块模型最大似然估计及其变分近似的渐近正态性AI 解读

Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the…