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Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes.

ABSTRACT: The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.

SUBMITTER: Montes F

PROVIDER: S-EPMC7048861 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes.

Montes Felipe F Jaramillo Ana María AM Meisel Jose D JD Diaz-Guilera Albert A Valdivia Juan A JA Sarmiento Olga L OL Zarama Roberto R

Scientific reports 20200228 1

The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in sp ...[more]

PMID: 32111953

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Project description:We propose a theoretical framework for the study of spreading processes in structured metapopulations, with heterogeneous agents, subjected to different recurrent mobility patterns. We propose to represent the heterogeneity in the composition of the metapopulations as layers in a multiplex network, where nodes would correspond to geographical areas and layers account for the mobility patterns of agents of the same class. We analyze classical epidemic models within this framework and obtain an excellent agreement with extensive Monte Carlo simulations. This agreement allows us to derive analytical expressions of the epidemic threshold and to face the challenge of characterizing a real multiplex metapopulation, the city of Medellín in Colombia, where different recurrent mobility patterns are observed depending on the socioeconomic class of the agents. Our framework allows us to unveil the geographical location of those patches that trigger the epidemic state at the critical point. A careful exploration reveals that social mixing between classes and mobility crucially determines these critical patches and, more importantly, it can produce abrupt changes of the critical properties of the epidemic onset. Summary The spread of disease is strongly driven by how humans move and segregate across cities, regions, and countries. Understanding how epidemics arise from the interplay of these behaviors is crucial for implementing efficient containment and prevention policies. In this work, we propose a theoretical framework for the spread of pathogens that incorporates the real patterns of mobility, demographics, and human diversity observed in urban environments. Our framework represents various populations in a community as layers in a multiplex network, where nodes correspond to geographical areas and layers account for movement patterns. With this framework, we analyze a real urban system, the city of Medellin in Colombia, considering the interplay between six socioeconomic classes displaying disparate demographic segregation and mobility habits. Our framework can identify those neighborhoods and classes that trigger epidemic outbreaks. Remarkably, we observe that small changes to both mobility and social mixing among these subpopulations can trigger abrupt changes. As a consequence, containment strategies targeting a certain neighborhood can quickly change from efficient to useless. Finally, we provide a physical interpretation of the abrupt changes of those patches driving the unfolding of epidemics based on the effective number of contacts of agents. Our framework provides a reliable, time-saving platform for analyzing the spread of pathogens among various populations, allowing researchers to identify areas critical to the unfolding of diseases. With further improvements, our formalism could be extended to accommodate more sophisticated commuting patterns and epidemic models.

Dataset Information

Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes.

Publications

Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes.

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