Vital nodes identification in complex networks
By Linyuan Lu, Duanbing Chen, Xiaolong Ren, Qian-Ming Zhang, Yi-Cheng Zhang, Tao Zhou. arXiv.org, 2016
1,177 citations recorded by the source corpus.
Real networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify vital nodes is thus very significant, allowing us to control the outbreak of epidemics, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The vital nodes identification attracts increasing attentions from both computer science and physical societies, with algorithms ranging from simply counting the immediate neighbors to complicated machine learning and message passing approaches. In this review, we clarify the concepts and metrics, classify the problems and methods, as well as review the important progresses and describe the state of the art. Furthermore, we provide extensive empirical analyses to compare well-known methods on disparate real networks, and highlight the future directions. In despite of the emphasis on physics-rooted approaches, the unification of the language and comparison with cross-domain methods would trigger interdisciplinary solutions in the near future.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/f1681a359b623f69aecfc0270238b8200cc375e3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
In Statistical and Nonlinear Physics
- M. A. Porter
- Naoki Masuda
- Renaud Lambiotte
- Matjaz Perc
- Timon Elmer
- M. Wichers
- Marko Marhl
- Sacha Epskamp
Citations in the corpus
- Network science of biological systems at different scales: A review. (2017, background): “[15] Lü L, Chen D, Ren XL, Zhang QM, Zhang YC, Zhou T.”
- Random walks and diffusion on networks (2016, background): “There are myriad ways to measure centralities in networks, especially for ranking nodes [44, 277], and new ones are published at a very rapid pace.”
- What do centrality measures measure in psychological networks? (2019, background): “…networks, in which edges have only two possible values, one (an edge) or zero (no edge), and still form the basis of many centrality indices (nowadays there are over 100 centrality measures to choose from; Lü et al.,2016) 2 , including the ones we will discuss below (Wasserman & Faust, 1994).”