Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition

Liu, Ying; Tang, Ming; Zhou, Tao; Do, Younghae

doi:10.1038/srep09602

Cited by 153 publications

(95 citation statements)

References 53 publications

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“…Many methods, in particular centrality measure based methods, have been proposed in the past that aim to model and identify the most influential spreaders in complex networks. Among them, K-shell Decomposition method678 and Expected Force method9 have shown better performance than others under various epidemiological models. To evaluate the accuracy of the aforementioned measures various techniques has been employed.…”

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confidence: 99%

“…Various centrality measures have been used to predict the epidemic outcomes of the nodes, with the basic assumption that more centrally the nodes are located in the network, the greater spreading power they will have6131415. The well-known centrality measures, and the most frequently used in this field, include: Degree16, Strength17, Betweenness1618, Closeness16, Eigenvector19, PageRank20 and K-shell67212223. A novel and more effective measure, known as Expected Force9, has been recently proposed with a particular aim for classifying nodes based on their influence in the network.…”

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confidence: 99%

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Super-Spreader Identification Using Meta-Centrality

Madotto

Liu

2016

Sci Rep

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Super-spreaders are the nodes of a network that can maximize their impacts on other nodes, e.g., in the case of information spreading or virus propagation. Many centrality measures have been proposed to identify such nodes from a given network. However, it has been observed that the identification accuracy based on those measures is not always satisfactory among different types of networks. In addition, the nodes identified by using single centrality are not always placed in the top section, where the super-spreaders are supposed to be, of the ranking generated by simulation. In this paper we take a meta-centrality approach by combining different centrality measures using a modified version of Borda count aggregation method. As a result, we are able to improve the performance of super-spreader identification for a broad range of real-world networks. While doing so, we discover a pattern in the centrality measures involved in the aggregation with respect to the topological structures of the networks used in the experiments. Further, we study the eigenvalues of the Laplacian matrix, also known as Laplacian spectrum, and by using the Earth Mover’s distance as a metric for the spectrum, we are able to identify four clusters to explain the aggregation results.

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confidence: 99%

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confidence: 99%

Super-Spreader Identification Using Meta-Centrality

Madotto

Liu

2016

Sci Rep

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“…We review briefly the degree centrality and the k-shell index for completeness. They are efficient measures for identify influential spreaders [37][38][39]. We will compare the performance of our newly defined node strength and s-shell index with these methods.…”

Section: Centralities Spreading Model and Evaluation Methodsmentioning

confidence: 99%

Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights

Liu

Tang

et al. 2017

Phys. Rev. E

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We propose an efficient and accurate measure for ranking spreaders and identifying the influential ones in spreading processes in networks. While the edges determine the connections among the nodes, their specific role in spreading should be considered explicitly. An edge connecting nodes i and j may differ in its importance for spreading from i to j and from j to i. The key issue is whether node j, after infected by i through the edge, would reach out to other nodes that i itself could not reach directly. It becomes necessary to invoke two unequal weights w_{ij} and w_{ji} characterizing the importance of an edge according to the neighborhoods of nodes i and j. The total asymmetric directional weights originating from a node leads to a novel measure s_{i}, which quantifies the impact of the node in spreading processes. An s-shell decomposition scheme further assigns an s-shell index or weighted coreness to the nodes. The effectiveness and accuracy of rankings based on s_{i} and the weighted coreness are demonstrated by applying them to nine real-world networks. Results show that they generally outperform rankings based on the nodes' degree and k-shell index while maintaining a low computational complexity. Our work represents a crucial step towards understanding and controlling the spread of diseases, rumors, information, trends, and innovations in networks.

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“…These findings challenge the previous predominate focus on the number of connections. The simple yet effective measure k-core has inspired several generalizations in consideration of the detailed local environment in the vicinity of high k-core nodes [95,50,51,54].…”

Section: Topological Measuresmentioning

confidence: 99%

Theories for Influencer Identification in Complex Networks

Pei

Morone

Makse

2018

Computational Social Sciences

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In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.

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Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition

Cited by 153 publications

References 53 publications

Super-Spreader Identification Using Meta-Centrality

Super-Spreader Identification Using Meta-Centrality

Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights

Theories for Influencer Identification in Complex Networks

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