Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Li, Longjie; Bai, Shenshen; Leng, Mingwei; Wang, Lu; Chen, Xiaoyun

doi:10.1155/2018/3579758

Cited by 9 publications

(6 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, on most of networks, Precision shows downward trend when the size of L increases. This is because that with the increasing of L, the probability to uncover relevant items will decrease, and then the value of Precision will lower [22].…”

Section: B Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Top-${L}$ Missing Links: An Improved Local Naïve Bayes Model

Leng

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

The problem of link prediction has captured considerable attention from various disciplines due to its wide range of applications. A multitude of link prediction methods have been proposed with various techniques. The local Naïve Bayes (LNB) model is an effective one, which discriminates the contribution of different common neighbors by a role function. This paper proposes a new link prediction method, which further enhances the accuracy of the LNB model by considering the local community links and the degree of seed nodes. The experimental results on 12 real-world networks demonstrate that the proposed method outperforms the compared methods in the top-L link prediction task.

show abstract

Section: B Results and Discussionmentioning

confidence: 99%

“…higher similarity score are more likely to be linked [12], [22]. To compute the similarity score of a pair of nodes, a straightforward strategy is to count the number of shared neighbors of these two nodes.…”

Section: Introductionmentioning

confidence: 99%

Predicting Top-${L}$ Missing Links: An Improved Local Naïve Bayes Model

Leng

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Twelve similarity indices are introduced in Section 2.2 , among which each index exploits one or two structural features of networks [ 36 ]. The fusion of these indices can incorporate multiple structural features to improve prediction performance.…”

Section: Proposed Framework: Rf-rfe-sellpmentioning

confidence: 99%

Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning

Wang

Jiao

Wang

2022

Entropy

View full text Add to dashboard Cite

Link prediction is an important task in the field of network analysis and modeling, and predicts missing links in current networks and new links in future networks. In order to improve the performance of link prediction, we integrate global, local, and quasi-local topological information of networks. Here, a novel stacking ensemble framework is proposed for link prediction in this paper. Our approach employs random forest-based recursive feature elimination to select relevant structural features associated with networks and constructs a two-level stacking ensemble model involving various machine learning methods for link prediction. The lower level is composed of three base classifiers, i.e., logistic regression, gradient boosting decision tree, and XGBoost, and their outputs are then integrated with an XGBoost model in the upper level. Extensive experiments were conducted on six networks. Comparison results show that the proposed method can obtain better prediction results and applicability robustness.

show abstract

“…Consequently, the AHP method is more suitable for such a situation. It is exploited for defining the weight of each criterion using the pair-wise comparison [20]. The process of this model is mainly based on different steps: The first step consists of structuring the decision hierarchy considering the essential objective of the study and determining the criteria and subcriteria.…”

Section: Multicriteria Decision Analysis (Mcda)mentioning

confidence: 99%

Efficient Routing Approach Using a Collaborative Strategy

Aziz

Aznaoui

2020

Journal of Sensors

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are a huge number of sensors, which are distributed in area monitoring to collect important signals. WSNs are widely used in several applications such as home automation, environment, and healthcare monitoring. However, most of these applications face various difficulties due to sensor design. Therefore, the major challenge of designing WSNs is saving the energy consumed during communication and extending the network lifetime. Multicriteria Decision Analysis (MCDA) methods have been exploited for saving network energy. However, the majority of researches focus on the Cluster Head (CH) selection. In this paper, we aim to enhance the process of forwarder selection using an efficient combined multicriteria model. The proposed scheme improved the intercluster communication by controlling the distance separating CHs from the sink node. To minimize the cluster density, this work consists of activating only sensor nodes that detect enough strong signals. The activation phase presents a fault-tolerant technique to succeed in the communication process. Moreover, the proposed work is aimed at selecting the most efficient hops, which are responsible for routing data to the sink using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. Simulation results proved that our new protocol maximized the residual energy by 15% and 25% and the network lifetime by 35% and 47% compared to the Distributed Clustering Protocol using Voting and Priority (DCPVP) and Low-Energy Adaptive Clustering Hierarchy (LEACH), respectively.

show abstract

Finding Missing Links in Complex Networks: A Multiple‐Attribute Decision‐Making Method

Cited by 9 publications

References 57 publications

Predicting Top-${L}$ Missing Links: An Improved Local Naïve Bayes Model

Predicting Top-${L}$ Missing Links: An Improved Local Naïve Bayes Model

Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning

Efficient Routing Approach Using a Collaborative Strategy

Contact Info

Product

Resources

About