We propose an unsupervised keyphrase extraction model that encodes topical
information within a multipartite graph structure. Our model represents
keyphrase candidates and topics in a single graph and exploits their mutually
reinforcing relationship to improve candidate ranking. We further introduce a
novel mechanism to incorporate keyphrase selection preferences into the model.
Experiments conducted on three widely used datasets show significant
improvements over state-of-the-art graph-based models.

本研究提出了一种无监督的关键词提取模型，该模型通过多方图结构将主题信息进行编码，并利用关键词和主题之间的相互关系来提高候选关键词的排名，同时引入了新颖的机制来纳入关键词选择偏好。在三个广泛使用的数据集上的实验表明，该模型相比于现有的基于图形的模型具有显著的改进。

多部分图的无监督关键词提取

Unsupervised Keyphrase Extraction with Multipartite Graphs

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。