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Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties — arXiv:2610.02872

Source: arXiv
Original: https://arxiv.org/abs/2610.02872
License (as archived): CC0-1.0
Archived: 2026-10-05T23:09:58.344Z
SHA-256 of the archived text: a2e88a3ae5e4e8a585271bb92d0ac672cff438512823fbee571651b8e3006d79
Stable URL of this page: https://metraipolis.com/documento/dc_8ea2eb653724

How to cite

Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties — arXiv:2610.02872 — arXiv, https://arxiv.org/abs/2610.02872 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8ea2eb653724 (Metraipolis archive dc_8ea2eb653724, sha256 a2e88a3ae5e4e8a585271bb92d0ac672cff438512823fbee571651b8e3006d79)

BibTeX

@misc{dc8ea2eb653724,
  title = {Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties — arXiv:2610.02872},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.02872},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8ea2eb653724; sha256 a2e88a3ae5e4e8a585271bb92d0ac672cff438512823fbee571651b8e3006d79},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.02872. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their interaction, and a sign-composition effect of reallocating treatment between the two types at a fixed total, and give their identification formulas. Under sign-blind assignment, we show how ignoring signs mixes the effects of the two tie types. We propose SiDE (Signed-exposure Doubly robust Estimator), which combines sign-specific outcome models with exposure probabilities induced by individual treatment assignment. We establish double robustness of its score and assess approximate intervals that account for overlapping neighborhoods. Semi-synthetic experiments on six real signed networks demonstrate accurate effect estimation and examine the limits of interval coverage. An exploratory reanalysis of published school-experiment data yields a positive estimate of the peer effect through spend-time ties on wristband wearing, but the intervals for all four effects include zero after adjustment for multiple comparisons. This framework can inform network intervention design by showing when influences through the two tie types reinforce or offset one another.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.02872 — 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 2026-10-05T23:01:38.646Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).