How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao, Tong; Shan, Wei; Zhou, Chang

doi:10.3390/e19110614

Cited by 53 publications

(40 citation statements)

References 66 publications

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“…For the binary karate club network, all vertex centralities identify either vertex 1 or vertex 34 as most important. These findings confirm previous studies [62][63][64][65] .…”

Section: A Zachary's Karate Club Networksupporting

confidence: 93%

Centrality-based identification of important edges in complex networks

Bröhl

Lehnertz

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Centrality is one of the most fundamental metrics in network science. Despite an abundance of methods for measuring centrality of individual vertices, there are by now only a few metrics to measure centrality of individual edges. We modify various, widely used centrality concepts for vertices to those for edges, in order to find which edges in a network are important between other pairs of vertices. Focusing on the importance of edges, we propose an edge-centrality-based network decomposition technique to identify a hierarchy of sets of edges, where each set is associated with a different level of importance. We evaluate the efficiency of our methods using various paradigmatic network models and apply the novel concepts to identify important edges and important sets of edges in a commonly used benchmark model in social network analysis, as well as in evolving epileptic brain networks.

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“…For the binary karate club network, all vertex centralities identify either vertex 1 or vertex 34 as most important. These findings confirm previous studies [62][63][64][65] .…”

Section: A Zachary's Karate Club Networksupporting

confidence: 93%

Centrality-based identification of important edges in complex networks

Bröhl

Lehnertz

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Measuring II of a host is essential to capturing the intricacies of pathogen sharing not covered by the conventional measures analysed above, namely the range and speed by which host species propagate their pathogens to their neighbours. In this, our measure is similar to other entropy-based measures [34], as well as to established influence measures such as Katz centrality [28, 29], but it focuses on the species influence in its neighbour space rather than across the whole network. To further assess the relationship between II and established centrality metrics, we performed a correlation analysis of centrality measures in our networks which revealed that II correlated (on average across all networks) with closeness [29] (0.88) and Opsahl closeness [24] (0.82) centralities, and showed least correlation with OBC (0.16).…”

Section: (C) Novel Metric Of Node Influence: Indirect Influencementioning

confidence: 99%

Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

2020

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Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal-pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.

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“…In [ 48 , 49 , 50 , 51 , 52 , 53 , 54 ], the authors have demonstrated that better results of quantitative analyses of influence can be obtained by using entropy theory. In our previous work [ 55 ], the same conclusion has been drawn that defined entropy centrality has proven far superior to other widely used methods, such as degree centrality, betweenness centrality, closeness centrality, Eigenvector centrality, and PageRank. It is clear that the ideal algorithm, free of any limitations or assumptions, does not exist.…”

Section: Introductionmentioning

confidence: 74%

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

Qiao

Shan

et al. 2018

Entropy

Self Cite

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Abstract:Measuring centrality has recently attracted increasing attention, with algorithms ranging from those that simply calculate the number of immediate neighbors and the shortest paths to those that are complicated iterative refinement processes and objective dynamical approaches. Indeed, vital nodes identification allows us to understand the roles that different nodes play in the structure of a network. However, quantifying centrality in complex networks with various topological structures is not an easy task. In this paper, we introduce a novel definition of entropy-based centrality, which can be applicable to weighted directed networks. By design, the total power of a node is divided into two parts, including its local power and its indirect power. The local power can be obtained by integrating the structural entropy, which reveals the communication activity and popularity of each node, and the interaction frequency entropy, which indicates its accessibility. In addition, the process of influence propagation can be captured by the two-hop subnetworks, resulting in the indirect power. In order to evaluate the performance of the entropy-based centrality, we use four weighted real-world networks with various instance sizes, degree distributions, and densities. Correspondingly, these networks are adolescent health, Bible, United States (US) airports, and Hep-th, respectively. Extensive analytical results demonstrate that the entropy-based centrality outperforms degree centrality, betweenness centrality, closeness centrality, and the Eigenvector centrality.

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How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Cited by 53 publications

References 66 publications

Centrality-based identification of important edges in complex networks

Centrality-based identification of important edges in complex networks

Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

A Novel Entropy-Based Centrality Approach for Identifying Vital Nodes in Weighted Networks

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