125. An Efficient Network-aware Direct Search Method for Influence Maximization
Invited abstract in session MC-13: Cardinality control in optimization problems for Data Science, stream Sparsity guarantee and cardinality-constrained (MI)NLPs.
Monday, 14:00-16:00Room: B100/6009
Authors (first author is the speaker)
| 1. | Matteo Bergamaschi
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| Department of Mathematics, University of Padua | |
| 2. | Francesco Rinaldi
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| Dipartimento di Matematica "Tullio Levi-Civita", Università di Padova | |
| 3. | Francesco Tudisco
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| Gran Sasso Science Institute | |
| 4. | Sara Venturini
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| Department of Mathematics "Tullio Levi-Civita", University of Padova |
Abstract
Networks play a crucial role in understanding real-world scenarios by revealing patterns, key nodes, and predicting information, disease, or innovation spread. The Influence Maximization problem, a significant challenge in network analysis, targets identifying influential individuals for various domains like marketing, public health, and social media. In this talk, we introduce a more comprehensive model for information propagation on networks, catering to a broader range of dynamics. Then we introduce Network-aware Direct Search (NaDS), a new direct search method tailored for the Influence Maximization problem. Unlike conventional methods, our approach guarantees good performances in diverse settings where traditional approaches falter, even in large-scale networks with thousands of nodes.
Keywords
- Derivative-free optimization
- Nonlinear mixed integer optimization
Status: accepted
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