Influence maximisation beyond organisational boundaries

Stein, Sebastian; Eshghi, Soheil; Bellamy, Rachel K. E.; Jennings, Nicholas R.

doi:10.1109/uic-atc.2017.8397422

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Since network topology has a clear role in influence spread, several studies have considered how to leverage features of nodes and the topology to maximise (or minimise) spread, such as identifying individuals with high degree or centrality [17,19,[44][45][46][47][48][49]. However, these approaches assume that a small set of individuals (called seed nodes) are selected as the source for a concept spreading.…”

Section: Spreading Models and Networkmentioning

confidence: 99%

An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

Archbold,

Clohessy,

Herath

et al. 2024

PLoS ONE

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Cardiovascular disease (CVD) is the leading cause of mortality globally, and is the second main cause of mortality in the UK. Four key modifiable behaviours are known to increase CVD risk, namely: tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol. Behaviours that increase the risk of CVD can spread through social networks because individuals consciously and unconsciously mimic the behaviour of others they relate to and admire. Exploiting these social influences may lead to effective and efficient public health interventions to prevent CVD. This project aimed to construct and validate an agent-based model (ABM) of how the four major behavioural risk-factors for CVD spread through social networks in a population, and examine whether the model could be used to identify targets for public health intervention and to test intervention strategies. Previous ABMs have typically focused on a single risk factor or considered very small populations. We created a city-scale ABM to model the behavioural risk-factors of individuals, their social networks (spousal, household, friendship and workplace), the spread of behaviours through these social networks, and the subsequent impact on the development of CVD. We compared the model output (predicted CVD events over a ten year period) to observed data, demonstrating that the model output is realistic. The model output is stable up to at least a population size of 1.2M agents (the maximum tested). We found that there is scope for the modelled interventions targeting the spread of these behaviours to change the number of CVD events experienced by the agents over ten years. Specifically, we modelled the impact of workplace interventions to show that the ABM could be useful for identifying targets for public health intervention. The model itself is Open Source and is available for use or extension by other researchers.

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Section: Spreading Models and Networkmentioning

confidence: 99%

An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

Archbold,

Clohessy,

Herath

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Therefore, how to effectively solve the influence maximization problem with limited or unknown network information is a critical subject that has attracted much attention over the past years [31][32][33]. Reconstructing the network through strategic sampling of a subset of nodes or edges within the unknown network to gather pertinent information is a feasible solution.…”

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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Influence maximisation beyond organisational boundaries

Cited by 2 publications

References 24 publications

An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

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

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