# Text characterization based on recurrence networks arXiv: https://arxiv.org/abs/2201.06665 ยท HTML: https://mlsanity.com/paper/2201.06665/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 62 references that are in the map. - First posted on arXiv 17 Jan 2022. ## Builds on (most cited first, 29 of 29) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016) - [A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics](https://mlsanity.com/paper/1812.08092/) (2018) - [In narrative texts punctuation marks obey the same statistics as words](https://mlsanity.com/paper/1604.00834/) (2016) - [Authorship Attribution through Function Word Adjacency Networks](https://mlsanity.com/paper/1406.4469/) (2014) - [Context Reinforced Neural Topic Modeling over Short Texts](https://mlsanity.com/paper/2008.04545/) (2020) - [Linguistic data mining with complex networks: a stylometric-oriented approach](https://mlsanity.com/paper/1808.05439/) (2018) - [Complex systems: features, similarity and connectivity](https://mlsanity.com/paper/1606.05400/) (2016) - [Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts](https://mlsanity.com/paper/1704.08088/) (2017) - [Force2Vec: Parallel force-directed graph embedding](https://mlsanity.com/paper/2009.10035/) (2020) - [Mapping computational thinking mindsets between educational levels with cognitive network science](https://mlsanity.com/paper/2007.09402/) (2020) - [DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning](https://mlsanity.com/paper/2110.13710/) (2021) - [Using network science and text analytics to produce surveys in a scientific topic](https://mlsanity.com/paper/1506.05690/) (2015) - [Authorship attribution via network motifs identification](https://mlsanity.com/paper/1607.06961/) (2016) - [Authorship Attribution Based on Life-Like Network Automata](https://mlsanity.com/paper/1610.06498/) (2016) - [Modeling the average shortest path length in growth of word-adjacency networks](https://mlsanity.com/paper/1409.4714/) (2014) - [A complex network approach to stylometry](https://mlsanity.com/paper/1506.09107/) (2015) - [Network analysis of named entity co-occurrences in written texts](https://mlsanity.com/paper/1509.05281/) (2015) - [Paragraph-based complex networks: application to document classification and authenticity verification](https://mlsanity.com/paper/1806.08467/) (2018) - [Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing](https://mlsanity.com/paper/2001.02621/) (2020) - [Concentric Network Symmetry](https://mlsanity.com/paper/1407.0224/) (2014) - [Concentric network symmetry grasps authors' styles in word adjacency networks](https://mlsanity.com/paper/1504.02162/) (2015) - [On the "Calligraphy" of Books](https://mlsanity.com/paper/1705.10415/) (2017) - [An Image Analysis Approach to the Calligraphy of Books](https://mlsanity.com/paper/1708.07265/) (2017) - [On the Stability of Citation Networks](https://mlsanity.com/paper/2105.01693/) (2021) ## Built on by (most cited first, 4 of 4) - [Complex systems approach to natural language](https://mlsanity.com/paper/2401.02772/) (2024) - [Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce](https://mlsanity.com/paper/2409.00483/) (2024) - [Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students](https://mlsanity.com/paper/2305.18320/) (2023) - [Using virtual edges to extract keywords from texts modeled as complex networks](https://mlsanity.com/paper/2205.02172/) (2022) ## Abstract (arXiv) Several complex systems are characterized by presenting intricate characteristics taking place at several scales of time and space. These multiscale characterizations are used in various applications, including better understanding diseases, characterizing transportation systems, and comparison between cities, among others. In particular, texts are also characterized by a hierarchical structure that can be approached by using multi-scale concepts and methods. The multiscale properties of texts constitute a subject worth further investigation. In addition, more effective approaches to text characterization and analysis can be obtained by emphasizing words with potentially more informational content. The present work aims at developing these possibilities while focusing on mesoscopic representations of networks. More specifically, we adopt an extension to the mesoscopic approach to represent text narratives, in which only the recurrent relationships among tagged parts of speech (subject, verb and direct object) are considered to establish connections among sequential pieces of text (e.g., paragraphs). The characterization of the texts was then achieved by considering scale-dependent complementary methods: accessibility, symmetry and recurrence signatures. In order to evaluate the potential of these concepts and methods, we approached the problem of distinguishing between literary genres (fiction and non-fiction). A set of 300 books organized into the two genres was considered and were compared by using the aforementioned approaches. All the methods were capable of differentiating to some extent between the two genres. The accessibility and symmetry reflected the narrative asymmetries, while the recurrence signature provided a more direct indication about the non-sequential semantic connections taking place along the narrative.