ChoiceRank: Identifying Preferences from Node Traffic in Networks

TL;DR

ChoiceRank introduces a scalable method to infer edge transition probabilities in large networks from only aggregate node traffic data, leveraging a preference learning model based on Luce's axiom.

Contribution

The paper presents ChoiceRank, an iterative algorithm that efficiently recovers transition probabilities in massive networks using only node-level traffic, regardless of network structure.

Findings

Successfully recovers transition probabilities in clickstream datasets

Scales to networks with billions of nodes and edges

Applies to mobility networks like NYC bicycle-sharing data

Abstract

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.