Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the $O(n)$ marginal counts of node visits are a
sufficient statistic for the $O(n^2)$ transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.

考虑到只观察到节点级别的交通流量并学习边缘转换概率，我们将其描述为偏好学习问题，并研究选择遵循卢斯公理的模型。我们表明如何使推断问题具有很好的形式，无论网络结构如何，并提出了 ChoiceRank 算法，可扩展到包含数十亿节点和边缘的网络。该模型应用于两个点击流数据集，成功地恢复了转换概率，仅使用网络结构和节点级别的流量数据。最后，我们还考虑了对移动网络的应用，并将该模型应用于纽约市的共享单车系统一年的骑行数据。