Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Understanding how users navigate in a network is of high interest in many applications.  consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.  cast it as a preference learning problem, and  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.  show how to make the inference problem ll-posed regardless of the network's structure, and  present ChoiceRank, an iterative algorithm that scales to networks that contains billions of nodes and edges.  apply the model to a month-long clickstream of the English Wikipedia and one year of rides on New York City's bicycle-sharing system. In both cases,  successfully recover the transition probabilities using only the network structure and marginal (node-level) traffic data.
['Lucas Maystre', 'Matthias Grossglauser']
I am writing a research paper on a idea ChoiceRank: Identifying Preferences from Node Traffic in Networks
[{"from": "human", "value": "I am writing a research paper on a idea ChoiceRank: Identifying Preferences from Node Traffic in Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Understanding how users navigate in a network is of high interest in many applications.  consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.  cast it as a preference learning problem, and  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.  show how to make the inference problem ll-posed regardless of the network's structure, and  present ChoiceRank, an iterative algorithm that scales to networks that contains billions of nodes and edges.  apply the model to a month-long clickstream of the English Wikipedia and one year of rides on New York City's bicycle-sharing system. In both cases,  successfully recover the transition probabilities using only the network structure and marginal (node-level) traffic data."}]