Influencing dynamics on social networks without knowledge of network microstructure

Garrod, Matthew; Jones, Nick

doi:10.1098/rsif.2021.0435

Cited by 5 publications

(3 citation statements)

References 70 publications

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“…In addition, other algorithms are also used to solve various practical influence maximization problems, such as evolutionary algorithms [26,27] and community-based strategies [28]. However, in many real-world scenarios, obtaining full network information is generally impractical and could be extremely expensive due to privacy protection and technical limitations [12,29,30].…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

Wang,

Wang

et al. 2024

J. R. Soc. Interface.

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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 Who do you think has more connections/friends? , and constructs the seeding set by those nodes with the most nominations. In this manner, the proposed method could acquire more information about the studied interaction network from the inference of respondents without surveying additional individuals. We evaluate our strategy on networks from various experimental datasets. Results show that the obtained seeds are much more influential compared to the one-hop strategy and other methods. We also show how the proposed approach could be implemented in field studies and potentially provide better interventions in real scenarios.

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Section: Introductionmentioning

confidence: 99%

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

Liu,

Wang,

Wang

et al. 2024

J. R. Soc. Interface.

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“…The literature [ 11 ] discovered the social sentiment of hot topics in science and technology by constructing an undirected weighted network. The literature [ 12 ] reveals the reliability of coarse-grained research methods in social media by collecting and analyzing large amounts of content. Literature [ 13 – 19 ] introduced the lack of scalability and efficiency of retrospective content analysis methods only coarse-grained.…”

Section: Introductionmentioning

confidence: 99%

Data driven identification of international cutting edge science and technologies using SpaCy

Gong

2022

PLoS ONE

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Difficulties in collecting, processing, and identifying massive data have slowed research on cutting-edge science and technology hotspots. Promoting these technologies will not be successful without an effective data-driven method to identify cutting-edge technologies. This paper proposes a data-driven model for identifying global cutting-edge science technologies based on SpaCy. In this model, we collected data released by 17 well-known American technology media websites from July 2019 to July 2020 using web crawling with Python. We combine graph-based neural network learning with active learning as the research method in this paper. Next, we introduced a ten-fold cross-check to train the model through machine learning with repeated experiments. The experimental results show that this model performed very well in entity recognition tasks with an F value of 98.11%. The model provides an information source for cutting-edge technology identification. It can promote innovations in cutting-edge technologies through its effective identification and tracking and explore more efficient scientific and technological research work modes.

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“…1 1 As an alternative to the combination of social network characteristics and evolutionary game theory that we employ, one could think of modeling social interaction directly (Cont and Löwe, 2010; Garrod and Jones, 2021). This approach would take account of the agents' heterogeneity with respect to individual attributes and attitudes, but it has the disadvantage that the underlying network structure is homogeneous.…”

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confidence: 99%

Public policy design and common property resources: A social network approach

Marco

Goetz

2023

American J Agri Economics

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This paper analyzes the extent to which two factors—social networks and the severity of the scarcity of a common property resource—affect norm‐complying behavior that favors cooperation. It assumes that those who comply with the social norm exercise social pressure on defectors. We develop an analytical framework that allows us to determine the minimum (maximum) share of norm‐complying agents at which social networks start (stop) having an influence on cooperation. Knowing these shares allows policymakers to identify the conditions under which legal and/or informal enforcement policies for cooperation are effective and how different types of social networks affect the design of these policies. We find that stable steady states exist in which compliers and defectors coexist (partial cooperation), but the stability of such states requires that the costs of coordination among compliers to exercise social pressure are high. Full cooperation is another possible steady state but is unlikely to prevail if the agents do not perceive the scarcity of the common property resource as severe. A numerical study, empirically calibrated for an aquifer in Spain, shows that subsidizing the compliers' costs of exerting social pressure may impede the attainment of a steady state based on partial cooperation. Although social networks can promote cooperation, their influence is limited. The minimum share of compliers for attaining cooperation can be reduced by informal enforcement policies by not more than 26%. We show that combinations of different types of informal enforcement policies should be applied cautiously because they may cancel each other out.

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Influencing dynamics on social networks without knowledge of network microstructure

Cited by 5 publications

References 70 publications

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

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

Data driven identification of international cutting edge science and technologies using SpaCy

Public policy design and common property resources: A social network approach

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