# Cognitive modelling with multilayer networks: Insights, advancements and future challenges

arXiv: https://arxiv.org/abs/2210.00500 · HTML: https://mlsanity.com/paper/2210.00500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 122 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [Community Detection in Multiplex Networks](https://mlsanity.com/paper/1910.07646/) (2019)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [Multiplex lexical networks reveal patterns in early word acquisition in children](https://mlsanity.com/paper/1609.03207/) (2016)
- [Reconstructing networks with unknown and heterogeneous errors](https://mlsanity.com/paper/1806.07956/) (2018)
- [Mapping higher-order network flows in memory and multilayer networks with Infomap](https://mlsanity.com/paper/1706.04792/) (2017)
- [Network comparison and the within-ensemble graph distance](https://mlsanity.com/paper/2008.02415/) (2020)
- [Structural measures for multiplex networks](https://mlsanity.com/paper/1308.3182/) (2013)
- [Feature-rich multiplex lexical networks reveal mental strategies of early language learning](https://mlsanity.com/paper/2201.05061/) (2022)
- [Multiplex model of mental lexicon reveals explosive learning in humans](https://mlsanity.com/paper/1705.09731/) (2017)
- [Brain network similarity:Methods and applications](https://mlsanity.com/paper/1908.10592/) (2019)
- [Distance entropy cartography characterises centrality in complex networks](https://mlsanity.com/paper/1802.10411/) (2018)
- [Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns](https://mlsanity.com/paper/2201.07538/) (2022)
- [An information-theoretic approach to hypergraph psychometrics](https://mlsanity.com/paper/2205.01035/) (2022)
- [Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems](https://mlsanity.com/paper/1507.08539/) (2015)
- [Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing](https://mlsanity.com/paper/2001.02621/) (2020)
- [Beyond pairwise network similarity: exploring Mediation and Suppression between networks](https://mlsanity.com/paper/2012.09006/) (2020)

## Abstract (arXiv)

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.