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A Novel Approach for the SDIR Epidemic Model on Online Social Networks — arXiv:2609.33682

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33682
License (as archived): CC0-1.0
Archived: 2026-10-07T22:34:15.555Z
SHA-256 of the archived text: 444f9c5b035ff2bc85dba05fca5d0b35d9ea0b17f194be3c6242d12b83c7bfc3
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How to cite

A Novel Approach for the SDIR Epidemic Model on Online Social Networks — arXiv:2609.33682 — arXiv (en), https://arxiv.org/abs/2609.33682 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff70cd96038d (Metraipolis archive dc_ff70cd96038d, sha256 444f9c5b035ff2bc85dba05fca5d0b35d9ea0b17f194be3c6242d12b83c7bfc3)

BibTeX

@misc{dcff70cd96038d,
  title = {A Novel Approach for the SDIR Epidemic Model on Online Social Networks — arXiv:2609.33682},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33682},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff70cd96038d; sha256 444f9c5b035ff2bc85dba05fca5d0b35d9ea0b17f194be3c6242d12b83c7bfc3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33682. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Information diffusion can be controlled by restricting or removing links (edges) in online social networks, as well as in real-world networks. To identify the most influential links to remove while minimizing diffusion, previous studies have proposed upper bounds for spreading processes in SIR and SIS models, using supermodularity and weighted matrices to identify critical links in contact networks. However, in some cases, existing upper bounds are not sufficiently tight to accurately capture the effect of important edges, as in the SDIR model of [14] (Khanh-Cho-Dung, Proceedings of 40th ICOIN, 2026). We therefore propose a tighter upper bound for controlling diffusion in the SDIR model by directly analyzing the dynamics of the two state vectors D and I in a $2N$-dimensional space. This approach yields an improved spectral-radius convergence condition and outperforms the previous method. Simulations on the synthetic Erdos-Renyi network and the real-world Haslemere dataset using a Greedy edge-deletion algorithm demonstrate its effectiveness for influence minimization on social networks.

Tema degli esploratori: governance digitale e beni comuni (retro-catalogo arXiv: annunci del 2026-09-29 in cs.SI).
Categorie arXiv: Social and Information Networks (cs.SI) ; Networking and Internet Architecture (cs.NI); Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2609.33682 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:12:43.498Z dalla pagina https://arxiv.org/catchup/cs.SI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).