Detailed simulations for flamelet modelling of SO<sub>x</sub> formation from coal

Wen, Xu; Stein, Oliver T.; Debiagi, Paulo; Scholtissek, Arne; Kronenburg, A.; Kempf, Andreas; Hasse, Christian

doi:10.1002/pamm.201900367

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Especially, link prediction based on the topological similarity [3][4][5][6] becomes the focus of the research fields. In the aspect of practical applications, link prediction can be widely used to do research on the relationships between proteins in the biological ecosystems, 7,8 the potential relationships in the social networks, [9][10][11][12] the user-commodity relationships in the e-commerce networks, 13 the related information in the collaborative information filtering systems, [14][15][16] the infrastructure planning in the transportation networks, the route planning in the aviation networks, 17,18 the optimal resource diffusion for suppressing disease spreading in the multiplex networks 19 and the applications in the wireless sensor networks, 20 etc.…”

Section: Introductionmentioning

confidence: 99%

Link prediction based on combined influence and effective path

Tian

Zhu

et al. 2019

Int. J. Mod. Phys. B

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Link prediction based on topological similarity in complex networks obtains more and more attention both in academia and industry. Most researchers believe that two unconnected endpoints can possibly make a link when they have large influence, respectively. Through profound investigations, we find that at least one endpoint possessing large influence can easily attract other endpoints. The combined influence of two unconnected endpoints affects their mutual attractions. We consider that the greater the combined influence of endpoints is, the more the possibility of them producing a link. Therefore, we explore the contribution of combined influence for similarity-based link prediction. Furthermore, we find that the transmission capability of path determines the communication possibility between endpoints. Meanwhile, compared to the local and global path, the quasi-local path balances high accuracy and low complexity more effectually in link prediction. Therefore, we focus on the transmission capabilities of quasi-local paths between two unconnected endpoints, which is called effective paths. In this paper, we propose a link prediction index based on combined influence and effective path (CIEP). A large number of experiments on 12 real benchmark datasets show that in most cases CIEP is capable of improving the prediction performance.

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