Service Facet Prioritisation Using Grey Relation Approach

2016 ◽  
Vol 12 (3-4) ◽  
pp. 238-241
Author(s):  
Sanjeev Verma ◽  
Ram Komal Prasad
Keyword(s):  
Relation Approach ◽  
Grey Relation

A Grey Relation Approach to the Integrated Process of QFD and QE

2011 ◽  
Vol 19 (1) ◽  
pp. 35-53 ◽  
Author(s):  
Ming-Shi Chen ◽  
Chen-Cheng Lin ◽  
Yuan-Yeuan Tai ◽  
Ming-Chyuan Lin
Keyword(s):  
Integrated Process ◽  
Relation Approach ◽  
Grey Relation

A Grey Relation Approach for Selection of Industrial Robot

2018 ◽  
Author(s):  
Kamalakanta Muduli ◽  
John Pumwa ◽  
Devendra K. Yadav ◽  
Raj Kumar ◽  
Sushanta Tripathy
Keyword(s):  
Industrial Robot ◽  
Relation Approach ◽  
Grey Relation ◽  
Selection Of

Fuzzy grey relation analysis on torque influence factors of alternating current electrodyna-mometer

2013 ◽  
Vol 26 (8) ◽  
pp. 693-698 ◽  
Author(s):  
Zhigang Zhang ◽  
Guixiang Zhang ◽  
Teng Liu ◽  
Cheng Qian ◽  
Yuanwang Deng
Keyword(s):  
Influence Factors ◽  
Alternating Current ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Grey Relation

Towards effective link prediction: A hybrid similarity model

2020 ◽  
pp. 1-14
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Hongsheng Luo ◽  
Xiaoyun Chen
Keyword(s):  
Link Prediction ◽  
Structural Similarity ◽  
Research Direction ◽  
Structural Features ◽  
Proposed Model ◽  
Similarity Model ◽  
Weight Calculation ◽  
Stable Performance ◽  
Grey Relation ◽  
Important Research Direction

Link prediction is an important research direction in complex network analysis and has drawn increasing attention from researchers in various fields. So far, a plethora of structural similarity-based methods have been proposed to solve the link prediction problem. To achieve stable performance on different networks, this paper proposes a hybrid similarity model to conduct link prediction. In the proposed model, the Grey Relation Analysis (GRA) approach is employed to integrate four carefully selected similarity indexes, which are designed according to different structural features. In addition, to adaptively estimate the weight for each index based on the observed network structures, a new weight calculation method is presented by considering the distribution of similarity scores. Due to taking separate similarity indexes into account, the proposed method is applicable to multiple different types of network. Experimental results show that the proposed method outperforms other prediction methods in terms of accuracy and stableness on 10 benchmark networks.

Sign in / Sign up

Export Citation Format

Share Document