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Social structures and real-world incidents often influence contemporaryliterary fiction. Existing research in literary fiction analysis explains thesereal-world phenomena through the manual critical analysis of stories.Conventional Natural Language Processing (NLP) methodologies, includingsentiment analysis, narrative summarization, and topic modeling, havedemonstrated substantial efficacy in analyzing and identifying similaritieswithin fictional works. However, the intricate dynamics of characterinteractions within fiction necessitate a more nuanced approach thatincorporates visualization techniques. Character interaction graphs (ornetworks) emerge as a highly suitable means for visualization and informationretrieval from the realm of fiction. Therefore, we leverage characterinteraction graphs with NLP-derived features to explore a diverse spectrum ofsocietal inquiries about contemporary culture's impact on the landscape ofliterary fiction. Our study involves constructing character interaction graphsfrom fiction, extracting relevant graph features, and exploiting these featuresto resolve various real-life queries. Experimental evaluation of influentialBengali fiction over half a century demonstrates that character interactiongraphs can be highly effective in specific assessments and informationretrieval from literary fiction. Our data and codebase are available athttps://cutt.ly/fbMgGEM

Understanding Social Structures from Contemporary Literary Fiction using  Character Interaction Graph -- Half Century Chronology of Influential Bengali  Writers