Tanasescu et al. – Closeness and betweenness centralities and #GraphPoem applications – DS² 2021
Channel: CEFISES at UCLouvain
Date: 2021-03-19

The talk “Closeness and betweenness centralities for multiplexes and their #GraphPoem applications” by Chris Tanasescu, Nicolas Burny, Jean Vanderdonckt (Université catholique de Louvain), Diana Inkpen, and Vaibhav Kesarwani (University of Ottawa), recorded at the DS² 2021 online conference, March 18, 2021.


In 2018, the first author of the paper launched—together with a number of co-editors—a computationally assembled poetry anthology (a concept and term coined by the first author), “US” Poets Foreign Poets (see https://bit.ly/36vTeLE, https://bit.ly/3cplmDJ, and https://bit.ly/36o8Hxd). The anthology featured works of digital as well as page-based poetry assembled as diction-representing networks and used natural language processing (NLP) algorithms developed as part of the Graph Poem project (https://bit.ly/3j4eM6T) at University of Ottawa to automatically expand the initial corpus. The poems selected for the corpus enlargement had particular diction that placed them, just like certain already existing poems in the corpus, in particular positions in the resulting expanding network, namely, ranking low in closeness centrality but high in betweenness centrality. The current development of the Graph Poem at UCLouvain involved moving on to multilayer networks in representing the corpus in the anthology (and other poetry corpora) and computing closeness and betweenness centralities. We kept the layer used for the above cited anthology and added another one quantifying rhymes and representing correlations between the vectors of such quantifications for each poem. We used the rhyme NLP classifier developed by the first author and his team at UOttawa (Tanasescu et al. 2021, forthcoming, https://bit.ly/2YyxytI) to generate this new layer and then the UCLouvain team developed and algorithms for computing closeness and betweenness centralities in multilayer networks in general and the applied them to the specific two-layer poetry multiplex. We present these algorithms, their relevance to graph theory applications in general, and their particular comparative outputs for our specific applications in computational poetry analysis and corpus analysis and expansion.