Link prediction in co-authorship networks based on hybrid content similarity metric

Chuan, Pham Minh; Sơn, Lê Hoàng; Ali, Mumtaz; Khang, Trần Đình; Hương, Lê Thanh; Dey, Nilanjan

doi:10.1007/s10489-017-1086-x

Cited by 83 publications

(37 citation statements)

References 39 publications

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“…Activity 4: Score calculation and ranking. (1) Firstly, we use two weighted similarity function formulas WCN and WJC [16] to calculate the weighting value of two nodes without connection in the G train . Next, we can obtain a ranking list in order to descend weighting value.…”

Section: Link Prediction Methodsmentioning

confidence: 99%

Link prediction in paper citation network to construct paper correlation graph

Liu

Kou

Yan

et al. 2019

J Wireless Com Network

123

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Nowadays, recommender system has become one of the main tools to search for users' interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user's need of keywords. Besides, to satisfy the users' requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors' information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

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Section: Link Prediction Methodsmentioning

confidence: 99%

Link prediction in paper citation network to construct paper correlation graph

Liu

Kou

Yan

et al. 2019

J Wireless Com Network

123

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“…Future studies will investigate a new method to accelerate the speed of HSSASCA as well as apply it for solving other constrained nonlinear optimization functions [62][63][64][65][66][67][68][69][70][71][72][73][74][75].…”

Section: Cantilever Beam Designmentioning

confidence: 99%

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

Singh¹,

Sơn²,

Chiclana³

et al. 2019

Engineering with Computers

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The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others.

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“…Two standard metrics are widely used to quantify the accuracy of link prediction methods: area under the receiver operating characteristic curve (AUC) [ 28 , 29 ] and precision [ 30 , 31 ]. In principle, a link prediction method gives each non-observed link a similarity score to quantify its existence likelihood.…”

Section: Metrics and Baselinesmentioning

confidence: 99%

A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

Li¹,

Liu²,

Chen³

et al. 2019

Entropy

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Recently, a number of similarity-based methods have been proposed for link prediction of complex networks. Among these indices, the resource-allocation-based prediction methods perform very well considering the amount of resources in the information transmission process between nodes. However, they ignore the information channels and their information capacity in information transmission process between two endpoints. Motivated by the Cannikin Law, the definition of information capacity is proposed to quantify the information transmission capability between any two nodes. Then, based on the information capacity, a potential information capacity (PIC) index is proposed for link prediction. Empirical study on 15 datasets has shown that the PIC index we proposed can achieve a good performance, compared with eight mainstream baselines.

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Link prediction in co-authorship networks based on hybrid content similarity metric

Cited by 83 publications

References 39 publications

Link prediction in paper citation network to construct paper correlation graph

Link prediction in paper citation network to construct paper correlation graph

A new fusion of salp swarm with sine cosine for optimization of non-linear functions

A Potential Information Capacity Index for Link Prediction of Complex Networks Based on the Cannikin Law

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