.png){kind=icon}
返回
Multilayer networks for text analysis with multiple data types
.png){kind=icon}
OA
AI
DOI：10.1140/epjds/s13688-021-00288-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks. To tackle the challenge of accounting for these different types of datasets, we propose a novel framework based on Multilayer Networks and Stochastic Block Models. The main innovation of our approach over other techniques is that it applies the same non-parametric probabilistic framework to the different sources of datasets simultaneously. The key difference to other multilayer complex networks is the strong unbalance between the layers, with the average degree of different node types scaling differently with system size. We show that the latter observation is due to generic properties of text, such as Heaps' law, and strongly affects the inference of communities. We present and discuss the performance of our method in different datasets (hundreds of Wikipedia documents, thousands of scientific papers, and thousands of E-mails) showing that taking into account multiple types of information provides a more nuanced view on topic- and document-clusters and increases the ability to predict missing links.
Keyword:
Stochastic block models
Multilayer networks
Natural language processing
Complex systems
Data science
期刊
IF:
2.5
论文数:
695
被引数:
1.6K
机构
引用论文
.png){kind=icon}
Digital health, cardiometabolic disease and ethnicity: an analysis of United Kingdom government policies from 2010 to 2022数字健康、 cardiometabolic disease 和 ethnicity：对2010年至2022年英国政府政策的分析OAAI
OAAI分享收藏分享收藏
查看更多内容