论文标题
学习复杂网络的曝光理论随机步行
Exposure theory for learning complex networks with random walks
论文作者
论文摘要
随机步行是探索和发现复杂网络的常见模型。尽管已经提出了许多算法来映射一个未知网络，但出现了一个互补的问题：在已知的网络中，在有限的时间内随机助行器最有可能发现哪些节点和边缘？在这里，我们介绍了曝光理论，这是一个统计力学框架，可预测几种类型网络（包括加权和时间上的网络）的节点和边缘的学习，并显示边缘学习遵循通用轨迹。虽然学习单个节点和边缘是嘈杂的，但暴露理论会产生高度准确的探索统计量。
Random walks are a common model for exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.
