International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Learning-Based Adaptive Hybrid Framework for Dynamic Seed Selection in Social Network Influence Maximization |
|---|---|
| ABSTRACT | Influence maximization looks for a select group of people who can start the spread of knowledge widely. Complementary influencer signals can be combined using fixed hybrid ranking, however it is predicated on feature weights being consistent across networks and diffusion conditions. In this paper, a learning-based adaptive hybrid framework for diversity-aware sequential seed selection is proposed, which learns seven feature weights from downstream diffusion feedback. Feature weighting is linked to a diffusion-and-overlap objective under Independent Cascade and Linear Threshold models through a simplex-constrained derivative-free search. Repeatable datasets, baselines, metrics, ablations, and complexity analysis are defined in the paper; numerical performance is purposefully withheld until the experiments are carried out. |
| AUTHOR | NIDHI GUPTA, DR. SOHIT AGARWAL Research Scholar, Department of Computer Engineering and Information Technology, Suresh Gyan Vihar University, Jaipur, Rajasthan, India Principal, ASTER School of Computer Science & Professor, Department of Computer Engineering and Information Technology, Suresh Gyan Vihar University, Jaipur, Rajasthan, India |
| VOLUME | 188 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1409018 |
| pdf/18_A Learning-Based Adaptive Hybrid Framework for Dynamic Seed Selection in Social Network Influence Maximization.pdf | |
| KEYWORDS | |
| References |
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