Abstract
Hadiths are the records of words, actions, and silent approval attributed to the Prophet Mohammed (PBUH). Their significance is second only to the Holy Quran within the Islamic faith. They have been collected, curated, and published by the most respected Islamic scholars through time. Each Hadith begins with a chain of narrators (sanad), distinguishing them from the main text (Matan), serving as a means for Islamic scholars to assess their strength and veracity. Most people read the approved six books of Hadiths, often focusing solely on the content (Matan) or browsing through topics, neglecting the profound intricacies hidden within the individual narrators’ backgrounds. Furthermore, the data is accessed without a visual user interface, imposing limitations due to the purely text-based nature. To alleviate this, we present Al-Ahadeeth, a visualization tool of the chain of narrators of Hadiths. Mapping narrators and narrated-to relations to nodes and edges in a directed graph, we use node size and edge thickness to show occurrence frequencies. Using the concept of generations, we place nodes on concentric rings based on the topological index to compute a 2D layout. Due to the high degree of interconnection in the community of narrators, traditional graph embedding techniques such as multi-dimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE) or uniform manifold approximation and projection (UMAP) tend to map the entire graph to a single cluster and give unsatisfactory results. In contrast, our approach uses the concept of generations to compute the layout of the graph which places nodes on concentric rings to indicate the generation.
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Acknowledgment
This work is part of a research project that is support by Qatar Research, Development and Innovation Council (QRDI) (formerly known as Qatar National Research Fund (QNRF)) under grand number: NPRP13S-0129-200194.
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Al-Absi, H.R.H. et al. (2024). Al-Ahadeeth: A Visualization Tool of the Hadiths’ Chain of Narrators. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2024 Posters. HCII 2024. Communications in Computer and Information Science, vol 2117. Springer, Cham. https://doi.org/10.1007/978-3-031-61953-3_1
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DOI: https://doi.org/10.1007/978-3-031-61953-3_1
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