# Multilayer Networks for Text Analysis with Multiple Data Types

arXiv: https://arxiv.org/abs/2106.15821 · HTML: https://mlsanity.com/paper/2106.15821/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [Structure and inference in annotated networks](https://mlsanity.com/paper/1507.04001/) (2015)
- [Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models](https://mlsanity.com/paper/1310.4378/) (2013)
- [Bayesian stochastic blockmodeling](https://mlsanity.com/paper/1705.10225/) (2017)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Nonparametric Bayesian inference of the microcanonical stochastic block model](https://mlsanity.com/paper/1610.02703/) (2016)
- [Community detection in networks: Structural communities versus ground truth](https://mlsanity.com/paper/1406.0146/) (2014)
- [Statistical laws in linguistics](https://mlsanity.com/paper/1502.03296/) (2015)
- [Merge-split Markov chain Monte Carlo for community detection](https://mlsanity.com/paper/2003.07070/) (2020)
- [Network structure, metadata and the prediction of missing nodes and annotations](https://mlsanity.com/paper/1604.00255/) (2016)
- [Multilayer stochastic block models reveal the multilayer structure of complex networks](https://mlsanity.com/paper/1411.1098/) (2014)
- [Revealing consensus and dissensus between network partitions](https://mlsanity.com/paper/2005.13977/) (2020)
- [A high-reproducibility and high-accuracy method for automated topic classification](https://mlsanity.com/paper/1402.0422/) (2014)
- [Consistencies and inconsistencies between model selection and link prediction in networks](https://mlsanity.com/paper/1705.07967/) (2017)
- [Community Detection in Bipartite Networks with Stochastic Blockmodels](https://mlsanity.com/paper/2001.11818/) (2020)
- [Co-word Maps and Topic Modeling: A Comparison Using Small and Medium-Sized Corpora (n < 1000)](https://mlsanity.com/paper/1511.03020/) (2015)
- [The Stochastic Topic Block Model for the Clustering of Vertices in Networks with Textual Edges](https://mlsanity.com/paper/1610.02427/) (2016)
- [Topic segmentation via community detection in complex networks](https://mlsanity.com/paper/1512.01384/) (2015)
- [Generalized Entropies and the Similarity of Texts](https://mlsanity.com/paper/1611.03596/) (2016)
- [Combining complex networks and data mining: why and how](https://mlsanity.com/paper/1604.08816/) (2016)
- [Scalable Text and Link Analysis with Mixed-Topic Link Models](https://mlsanity.com/paper/1303.7264/) (2013)

## Built on by (most cited first, 3 of 3)

- [Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis](https://mlsanity.com/paper/2208.06133/) (2022)
- [Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks](https://mlsanity.com/paper/2205.07532/) (2022)
- [Probabilistic emotion and sentiment modelling of patient-reported experiences](https://mlsanity.com/paper/2401.04367/) (2024)

## Abstract (arXiv)

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.