Aaqib Zahoor
Janibul Bashir
ABSTRACT
Influence Maximization addresses the challenge of identifying a small group of disseminators, known as seeds, essential for achieving maximal influence spread, particularly in viral marketing. This problem has now transitioned to the realm of temporal networks. Some approaches estimate influence spread and apply greedy or heuristic methods for seed selection, while others adapt to evolving networks over time. Our proposed approach, TBCELF, offers a two-fold solution. Firstly, it optimizes temporal seed selection, extending the principles of cost-effective lazy forward optimization. Secondly, it imposes a budget constraint, ensuring efficient seed selection within budgetary limits. We evaluate TBCELF on the manufacturing dataset and random graphs. Results show a 56.41% improvement in influence spread compared to the natural extension of the greedy algorithm to temporal networks, which highlights the improvement in seed quality by our proposed algorithm.
- David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the Spread of Influence through a Social Network. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Washington, D.C.) (KDD ’03). Association for Computing Machinery, New York, NY, USA, 137–146. https://doi.org/10.1145/956750.956769Google Scholar
Digital Library
- Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, and Natalie Glance. 2007. Cost-Effective Outbreak Detection in Networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Jose, California, USA) (KDD ’07). Association for Computing Machinery, New York, NY, USA, 420–429. https://doi.org/10.1145/1281192.1281239Google ScholarDigital Library
- Eric Yanchenko, Tsuyoshi Murata, and Petter Holme. 2023. Influence maximization on temporal networks: a review. arxiv:2307.00181 [cs.SI]Google Scholar
Index Terms
- TBCELF: Temporal Budget-Aware Influence Maximization
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