Academic Journal
Literary synergies: a Gephi-and Python-based network analysis of the International Booker Prize-winning novel ' Tomb of Sand '.
| Title: | Literary synergies: a Gephi-and Python-based network analysis of the International Booker Prize-winning novel ' Tomb of Sand '. |
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| Authors: | Sheoran, Kavi1 (AUTHOR), Paikane, Maithili1 (AUTHOR) |
| Source: | Digital Scholarship in the Humanities. Sep2026, Vol. 41 Issue 3, p1649-1671. 23p. |
| Subject Terms: | *Python programming language, *Data visualization software, *Fiction, *Literary interpretation, *Digital humanities |
| Abstract: | With a focus on the English translation of the novel Tomb of Sand by Geetanjali Shree (translated by Daisy Rockwell), this study applies network analysis to explore character relationships, thematic connections, and narrative structures. Using Gephi and Python, the research maps characters, themes, and settings as nodes connected by relational edges derived from manual textual extraction. Network metrics such as degree, betweenness, and clustering coefficient quantify the prominence of narrative elements and their interconnections. The analysis identifies key figures that anchor the story's social and thematic architecture and uncovers clusters that align with central motifs of identity, displacement, and generational conflict. Specifically, Mother (Ma) emerges as the most central node with the highest betweenness centrality (191.0 in Gephi; 0.498 in Python), while the thematic network (166 nodes, 348 edges) and modularity scores (Q ≈ 0.42) establish stable community structures and highlight spatial nodes such as Borders and Wagah Border as key connectors within the narrative. By integrating computational modelling with close reading, the study introduces a transparent, replicable framework for literary network analysis and broadens the scope of digital humanities research on translated South Asian fiction. [ABSTRACT FROM AUTHOR] |
| Database: | Academic Search Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: asx DbLabel: Academic Search Index An: 196803886 RelevancyScore: 1452 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1452.42749023438 |
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| Items | – Name: Title Label: Title Group: Ti Data: Literary synergies: a Gephi-and Python-based network analysis of the International Booker Prize-winning novel ' Tomb of Sand '. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sheoran%2C+Kavi%22">Sheoran, Kavi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Paikane%2C+Maithili%22">Paikane, Maithili</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Digital+Scholarship+in+the+Humanities%22">Digital Scholarship in the Humanities</searchLink>. Sep2026, Vol. 41 Issue 3, p1649-1671. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+visualization+software%22">Data visualization software</searchLink><br />*<searchLink fieldCode="DE" term="%22Fiction%22">Fiction</searchLink><br />*<searchLink fieldCode="DE" term="%22Literary+interpretation%22">Literary interpretation</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+humanities%22">Digital humanities</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With a focus on the English translation of the novel Tomb of Sand by Geetanjali Shree (translated by Daisy Rockwell), this study applies network analysis to explore character relationships, thematic connections, and narrative structures. Using Gephi and Python, the research maps characters, themes, and settings as nodes connected by relational edges derived from manual textual extraction. Network metrics such as degree, betweenness, and clustering coefficient quantify the prominence of narrative elements and their interconnections. The analysis identifies key figures that anchor the story's social and thematic architecture and uncovers clusters that align with central motifs of identity, displacement, and generational conflict. Specifically, Mother (Ma) emerges as the most central node with the highest betweenness centrality (191.0 in Gephi; 0.498 in Python), while the thematic network (166 nodes, 348 edges) and modularity scores (Q ≈ 0.42) establish stable community structures and highlight spatial nodes such as Borders and Wagah Border as key connectors within the narrative. By integrating computational modelling with close reading, the study introduces a transparent, replicable framework for literary network analysis and broadens the scope of digital humanities research on translated South Asian fiction. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=196803886 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/llc/fqag058 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1649 Subjects: – SubjectFull: Python programming language Type: general – SubjectFull: Data visualization software Type: general – SubjectFull: Fiction Type: general – SubjectFull: Literary interpretation Type: general – SubjectFull: Digital humanities Type: general Titles: – TitleFull: Literary synergies: a Gephi-and Python-based network analysis of the International Booker Prize-winning novel ' Tomb of Sand '. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sheoran, Kavi – PersonEntity: Name: NameFull: Paikane, Maithili IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2055768X Numbering: – Type: volume Value: 41 – Type: issue Value: 3 Titles: – TitleFull: Digital Scholarship in the Humanities Type: main |
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