OWA operator based link prediction ensemble for social network

We consider the maximum entropy constrained optimization problem associated with ordered weighted averaging (OWA) in the binomial decomposition framework. We begin by reviewing the analytic solution of the maximum entropy method proposed by Filev and Yager in 1995, and later by Fullér and Majlender in 2001. Next, we briefly review the binomial decomposition framework, which allows for an alternative parametric description of the OWA functions. The values of the binomial coefficients α j , j = 1 ,-0.15em … , n are uniquely determined by the weighting structure of the OWA function. We observe that for low orness values normalΩ ∈ [ 0 , 0.5 ], the optimal weights are decreasing, whereas they are increasing for high orness values normalΩ ∈ [ 0.5 , 1 ]. Moreover, we notice that the optimal values of the first and last weights have a wide range in [ 0 , 1 ], whereas the values of the other weights have more restricted ranges. As for the optimal α j , j = 1 , … , n coefficients, we find that their behavior with respect to orness values normalΩ ∈ [ 0 , 1 ] is very different for low/high orness. We illustrate graphically the optimal α j , j = 1 , … , n coefficients in two parts, first for low orness values normalΩ ∈ [ 0 , 0.5 ] and then for high orness values normalΩ ∈ [ 0.5 , 1 ]. We observe that the optimal α j , j = 1 , … , n for low orness values normalΩ ∈ [ 0 , 0.5 ] are all nonnegative and take values in the unit interval, independently of the dimension n. On the contrary, the optimal values of the α j , j = 1 , … , n coefficients for high orness values normalΩ ∈ [ 0.5 , 1 ] depend strongly on the dimension n, both in the complexity of their distribution and in the amplitude of their scale.

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“…Dividing expression (20) by expression (22), we obtain the expression for the optimal weights w i n , = 1,…,…”

Section: Filev and Yager's Analytic Construction Of Meowa Weightsmentioning

confidence: 99%

“…Applications of the maximum entropy approach can be found in various fields, see, for instance, Chang et al, Liaw et al, Yusoff and Merigó‐Lindahl, Chuu, He et al, and Kang et al. Two very recent applications of the maximum entropy method are described in Kim and Ahn and Brunelli and Fedrizzi.…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy ordered weighted averaging in the binomial decomposition framework

2018

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“…The OWA operator contains a parameterized family aggregation operators, such as the maximum, minimum and simple arithmetic mean. Since its proposed, the OWA operator has been widely used in many practical areas [2][3][4][5] . Due to the increasing complexity of modern society, the preference information provided by decision makers often express as interval numbers.…”

Section: Introductionmentioning

confidence: 99%

Note on continuous interval-valued intuitionistic fuzzy aggregation operator

Lin

Zhang

2017

Applied Mathematical Modelling

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Available online xxx Keywords: C-OWA operator Interval-valued intuitionistic fuzzy number Aggregation operator a b s t r a c t Recently, Zhou et al. proposed the C-IVIFOWA operator for aggregating interval-valued intuitionistic fuzzy numbers. The flaws of the C-IVIFOWA operator are illustrated by two counter-examples. The aim of this short communication is to show how the existing aggregation operator can be improved to increase accuracy. Some desirable properties of the revised C-IVIFOWA operator are studied in detail.

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“…Zeng et al [44] presented a method incorporating semi-supervised learning into the link prediction task to use the potential information in a large number of unlinked node pairs in networks. He et al [14] proposed a link-prediction ensemble algorithm based on an ordered weighted averaging operator. The algorithm assigns weights for nine local information-based link prediction algorithms and then aggregates their results to obtain final prediction scores.…”

Section: Related Workmentioning

confidence: 99%

A fast algorithm for predicting links to nodes of interest

Chen

2016

Information Sciences

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The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can.

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OWA operator based link prediction ensemble for social network

Cited by 117 publications

References 36 publications

Maximum entropy ordered weighted averaging in the binomial decomposition framework

Maximum entropy ordered weighted averaging in the binomial decomposition framework

Note on continuous interval-valued intuitionistic fuzzy aggregation operator

A fast algorithm for predicting links to nodes of interest

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