From Nodes to Notables a Graph Theoretic Framework for Uncovering Emerging Influencers

Title

From Nodes to Notables a Graph Theoretic Framework for Uncovering Emerging Influencers

Creator

Saxena, Anushka; Swaraj Khan, P.

Description

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.

Source

Proceedings of 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.1542-1547

Date

01-01-2026

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Centrality Measures; Community Detection; Graph Theory; Influencer Detection; Information Diffusion; Social Network Analysis

Coverage

Saxena A., Christ University, Dept. of Statistics and Data Science, Bengaluru, India; Swaraj Khan P., Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159237-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICVADV67766.2026.11469862

https://www.scopus.com/pages/publications/105037465760?origin=resultslist