Identifying influential spreaders in complex networks

By Maksim Kitsak, Lazaros K. Gallos, Shlomo Havlin, Fredrik Liljeros, Lev Muchnik, H. Eugene Stanley, Hernán A. Makse. 2010

2,502 citations recorded by the source corpus.

Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient "spreaders" in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people (high betweenness centrality). Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that-- in the case of infections that do not confer immunity on recovered individuals-- the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8f198d421e7d3445f36c3dc01375760ba32f6bf3

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In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Identifying influential spreaders in complex networks based on gravity formula (2015, background): “proposed a k -shell decomposition to identify the most influential spreaders based on the assumption that nodes in the same shell have similar influence and nodes in higher shells are likely to infect more nodes, which is found to be better than the degree centrality index in many real networks [1].”
The effect of social networks structure on innovation performance: A review and directions for research (2019, background): “The nodes surviving a high number of iterations in this “k-shell decomposition” process can be considered the core of the network, which has been shown to contain the most effective spreaders of information (Kitsak et al., 2010).”
Measuring user influence on Twitter: A survey (2015, methodology): “The first method is the k-shell decomposition algorithm, that has been applied to identify influential actors in epidemic dynamics [68].”
Dynamics of information diffusion and its applications on complex networks (2016, background): “Network topology, such as centrality [279], structural hole [281], k-shell [134], can statistically identify influential leaders with respect to their local roles in networks.”
The scaling of human interactions with city size (2012, background): “The degree and communication activity (call volume and number of calls) indicate how fast the state of a node may spread to nearby nodes [15, 40, 41], while the clustering largely determines its probability of propagating beyond the immediate neighbours [42, 43].”
Multilayer Analysis and Visualization of Networks (2014, background): “Such efforts have yielded numerous insights in many areas of science and technology [2–16].”
Social Network Sensors for Early Detection of Contagious Outbreaks (2010, background): “number of friends that is important with respect to flu risk, but also the number of friends of friends, friends of friends of friends, and so on [6].”
A framework for quantitative analysis of cascades on networks (2010, background): “However, empirical approaches are needed to validate assumptions made by these models.”
Network resilience (2020, background): “[777]; how the specie abundance evolves during a time period [778]; how many individuals are newly affected by or recovered [779] from a certain disease? Modelling such processes require determinations of equation forms and a set of parameters, which are not easy to achieve due to the complexity and”
Artificial intelligence in cancer target identification and drug discovery (2022, background): “suggested that high connectivity does not necessarily imply its essentiality, but also Kitsak et al.(129) argued that the location of nodes is more significant than the immediate neighbours to evaluate its spreading influence because degree centrality considers only direct interactions of a node but”
The Anatomy of a Scientific Rumor (2013, background): “those with higher k-coreness [39], and there is evidence of a positive correlation between k-coreness and the size of cascades of messages, suggesting that users at the core of the network are more likely to be the seeds of global chains of information diffusion [40].”