Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Saxena, Anushka; Swaraj Khan, P. Title A name given to the resource From Nodes to Notables a Graph Theoretic Framework for Uncovering Emerging Influencers Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.1542-1547 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVADV67766.2026.11469862" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVADV67766.2026.11469862</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037465760?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037465760?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Saxena A., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Swaraj Khan P., Bengaluru, India Description An account of the resource Identification of new effects in social networks is important for effective digital marketing, information dissemination and community engagement. This article introduces a new graph-theoretical framework designed to systematically reveal impressively by analyzing structural network properties. Our function benefits from the main centrality matrix - including degrees, beach, proximity and self vector center - to evaluate the effect of users in the complex network. In addition, we use social identity algorithms such as the Luven and label suggestions to identify opinions of opinions and important contacts in networking groups. The processing of data affecting recent progress in Graph Neural Network (GNNS) is integrated to limit the impressive identity in order to detect the treatment and the fine effect pattern. Experimental results accurately demonstrate the efficiency of our hybrid approaches in influencing pinpointing, leading to actionable insight into targeted marketing campaigns and the effect of impact monitoring is increased. The challenges related to interpretation and dynamic network situations are discussed for future research. 2026 IEEE. Subject The topic of the resource Centrality Measures; Community Detection; Graph Theory; Influencer Detection; Information Diffusion; Social Network Analysis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159237-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online