Abstract
Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively. The adaptive weighted link model is completely depends on the semi-local topological structure and thus can be calculated not only faster but also under the case where the global topology is not known, especially when the network is sparse, the time complexity is approximate linear. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks show that the adaptive weighted link model always perform the best in comparison with well-known state-of-the-art methods.
Highlights
Network science is playing an increasingly significant role in many domains [1]
The heterogeneous nature of real networks [2] asks for a crucial question: How to measure a node’s importance quantitatively in a dynamical process? A good answer is an efficient algorithm to identify influential spreaders in complex networks, which can help to better control the outbreak of an epidemic [3], optimize the use of limited resources to facilitate the dissemination of information [4], prevent catastrophic disruptions of power grid or the Internet [5], discover the candidates of drug target and essential proteins [6], find the important species for ecosystems [7], [8], and so on
We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively
Summary
Network science is playing an increasingly significant role in many domains [1]. The heterogeneous nature of real networks [2] asks for a crucial question: How to measure a node’s importance quantitatively in a dynamical process? A good answer is an efficient algorithm to identify influential spreaders in complex networks, which can help to better control the outbreak of an epidemic [3], optimize the use of limited resources to facilitate the dissemination of information [4], prevent catastrophic disruptions of power grid or the Internet [5], discover the candidates of drug target and essential proteins [6], find the important species for ecosystems [7], [8], and so on. Most known methods only make use of the structural information [9], which can be roughly classified into neighborhood-based centralities and path-based centralities. Some more potential methods that only use the semi-local structural information are proposed and perform much better than the above well-known state-of-the-art methods, such as Quasi-Laplacian centrality [16] (QC) and Local Gravity Model [17] (LGM). We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively.
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