Evidence map›Paper›PMID 38715322›Full record

ArticleJournal of the Royal Society, Interface2024

Individual-centralized seeding strategy for influence maximization in information-limited networks.

Yang Liu, Xiaoqi Wang, Xi Wang, Li Yan, Sinuo Zhao, Zhen Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yang LiuSchool of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University , Xi'an, 710072, China.ORCID 0000-0001-7604-8279
Xiaoqi WangSchool of Mechanical Engineering, Northwestern Polytechnical University , Xi'an, 710072, China.
Xi WangDepartment of Radiation Oncology, Stanford University School of Medicine , Palo Alto, CA, USA.
Li YanSchool of Computer Science, Northwestern Polytechnical University , Xi'an, 710072, China.
Sinuo ZhaoHonors College, Northwestern Polytechnical University , Xi'an, 710072, China.
Zhen WangSchool of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University , Xi'an, 710072, China.ORCID 0000-0002-8182-2852

Funding

National Natural Science Foundation of ChinaNational Science Fund for Distinguished Young Scholarsthe Fundamental Research Funds for the Central Universitiesthe Tencent Foundation and XPLORER PRIZE
6 · The paper itself

Abstract

Peer effects can directly or indirectly rely on interaction networks to drive people to follow ideas or behaviours triggered by a few individuals, and such effects can be largely improved by targeting the so-called influential individuals. In this article, we study the current most promising seeding strategy used in field experiments, the one-hop strategy, where the underlying interaction networks are generally too impractical or prohibitively expensive to be obtained, and propose an individual-centralized seeding approach to target influential seeds in information-limited networks. The presented strategy works by reasonable follow-up questions to respondents, such as

Indexed as

Models, TheoreticalHumanscomplex networksinfluence maximizationinformation-limited networksspreading dynamics

Identifiers

PMID38715322
PMCPMC11077013

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.