A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata
Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic <i>Mahabharata</i> was performed, in which it was prepossessed into ver...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Heritage |
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| Online Access: | https://www.mdpi.com/2571-9408/6/11/366 |
| _version_ | 1797459102815748096 |
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| author | Eren Gultepe Vivek Mathangi |
| author_facet | Eren Gultepe Vivek Mathangi |
| author_sort | Eren Gultepe |
| collection | DOAJ |
| description | Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic <i>Mahabharata</i> was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, <i>p</i> < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749. |
| first_indexed | 2024-03-09T16:46:38Z |
| format | Article |
| id | doaj.art-1585d5ca9cc24f16b86fd09783a97e39 |
| institution | Directory Open Access Journal |
| issn | 2571-9408 |
| language | English |
| last_indexed | 2024-03-09T16:46:38Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Heritage |
| spelling | doaj.art-1585d5ca9cc24f16b86fd09783a97e392023-11-24T14:45:00ZengMDPI AGHeritage2571-94082023-10-016117009703010.3390/heritage6110366A Quantitative Social Network Analysis of the Character Relationships in the MahabharataEren Gultepe0Vivek Mathangi1Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USADepartment of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USADespite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic <i>Mahabharata</i> was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, <i>p</i> < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.https://www.mdpi.com/2571-9408/6/11/366natural language processingliterary heritageword vectorssocial network analysischaracter networks<i>Mahabharata</i> |
| spellingShingle | Eren Gultepe Vivek Mathangi A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata Heritage natural language processing literary heritage word vectors social network analysis character networks <i>Mahabharata</i> |
| title | A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata |
| title_full | A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata |
| title_fullStr | A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata |
| title_full_unstemmed | A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata |
| title_short | A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata |
| title_sort | quantitative social network analysis of the character relationships in the mahabharata |
| topic | natural language processing literary heritage word vectors social network analysis character networks <i>Mahabharata</i> |
| url | https://www.mdpi.com/2571-9408/6/11/366 |
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