随机游动复杂网络学习的暴露理论
Exposure theory for learning complex networks with random walks
摘要：
随机游动是探索和发现复杂网络的常见模型。虽然已经提出了许多算法来绘制一个未知的网络，但一个互补的问题出现了：在一个已知的网络中，哪些节点和边最有可能在有限时间内被随机步行者发现？在这里，我们介绍了曝光理论，一个统计力学框架，预测在几种类型的网络，包括加权和时间的节点和边缘的学习，并显示边缘学习遵循一个普遍的轨迹。虽然单个节点和边的学习是嘈杂的，但暴露理论产生了对聚合探索统计的高度准确的预测。
英文摘要：
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.
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