scholarly journals Community Detection Based on Density Peak Clustering Model and Multiple Attribute Decision-Making Strategy TOPSIS

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jianjun Cheng ◽  
Xu Wang ◽  
Wenshuang Gong ◽  
Jun Li ◽  
Nuo Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Detection ◽  
Detection Algorithm ◽  
Multiple Attribute Decision Making ◽  
Density Peak ◽  
Clustering Model ◽  
Multiple Attribute ◽  
Density Peak Clustering ◽  
Community Detection Algorithm ◽  
Topsis Technique

Community detection is one of the key research directions in complex network studies. We propose a community detection algorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). First, the two-dimensional dataset, which is transformed from the network by taking the density and distance as the attributes of nodes, is clustered by using the DBSCAN algorithm, and outliers are determined and taken as the key nodes. Then, the initial community frameworks are formed and expanded by adding the most similar node of the community as its new member. In this process, we use TOPSIS to cohesively integrate four kinds of similarities to calculate an index, and use it as a criterion to select the most similar node. Then, we allocate the nonkey nodes that are not covered in the expanded communities. Finally, some communities are merged to obtain a stable partition in two ways. This paper designs some experiments for the algorithm on some real networks and some synthetic networks, and the proposed method is compared with some popular algorithms. The experimental results testify for the effectiveness and show the accuracy of our algorithm.

scholarly journals Sistem Pendukung Keputusan Pemberian Reward Pegawai Menggunakan Metode TOPSIS

2020 ◽  
Vol 18 (1) ◽  
pp. 11
Author(s):  
Aisyah Mutia Dawis
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Selection Process ◽  
Multiple Attribute Decision Making ◽  
Management Problem ◽  
Multiple Attribute ◽  
Ideal Solutions ◽  
Order Preference ◽  
Topsis Technique ◽  
A Company

Every company has management providing wages or rewards to employees. This is because employees are one of the resources that are used as a driving force in advancing a company. Besides, many companies provide rewards to their employees with the aim of motivating employees to help more. There is management problem in PKU Muhammadiyah Gamping Hospital for determining the number of rewards obtained by employees because many variables are determined. Therefore, the need of management information system can facilitate the Management of the PKU Muhammadiyah Gamping Hospital in determining decision making for providing rewards. One method that is often used in implementing decision support systems is Multiple Attribute Decision Making (MADM), focusing TOPSIS (Technique for Order Preference with Similarities to Ideal Solutions). By the implementation of the decision support system, PKU Muhammadiyah Gamping Hospital can carry out the selection process more efficiently.The test results by matching the employee data results at PKU Muhammadiyah Hospital obtained 95.83% accuracy so that this system can help the PKU Muhammadiyah Hospital in determining employee rewards.

scholarly journals Impact of weight indicators of TOPSIS technique in the selected district in the Slovak republic

2013 ◽  
Vol 2 (4) ◽  
pp. 61
Author(s):  
Roman Vavrek ◽  
Peter Adamišin
Keyword(s):  
Decision Making ◽  
Local Government ◽  
Decision Maker ◽  
Slovak Republic ◽  
Multiple Attribute Decision Making ◽  
Local Government Area ◽  
Decision Models ◽  
Multicriteria Decision ◽  
Multiple Attribute ◽  
Topsis Technique

The purpose of multicriteria decision models is to help decision maker to evaluate each alternative and to rank them in descending order of performance. This study analyses the concept of Multiple Attribute Decision Making for using in local government area. The aim of this paper is to analyse the concept of Multiple Attribute Decision Making for selecting the most efficient municipality in selected district in the Slovak Republic. Achieving this purpose, TOPSIS technique (in two variants) is used as decision making tools.

scholarly journals Multiple-Attribute Decision-Making Problem Using TOPSIS and Choquet Integral with Hesitant Fuzzy Number Information

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Harish Garg ◽  
Abazar Keikha ◽  
Hassan Mishmast Nehi
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Choquet Integral ◽  
Multiple Attribute Decision Making ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Information ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Collection Data ◽  
Topsis Technique

The paper aims are to present a method to solve the multiple-attribute decision-making (MADM) problems under the hesitant fuzzy set environment. In MADM problems, the information collection, aggregation, and the measure phases are crucial to direct the problem. However, to handle the uncertainties in the collection data, a hesitant fuzzy number is one of the most prominent ways to express uncertain and vague information in terms of different discrete numbers rather than a single crisp number. Additionally, to aggregate and to rank the collective numbers, a TOPSIS (“Technique for Order of Preference by Similarity to Ideal Solution”) and the Choquet integral (CI) are the useful tools. Keeping all these features, in the present paper, we combine the TOPSIS and CI methods for hesitant fuzzy information and hence present a method named as TOPSIS-CI to address the MADM problems. The presented method has been described with a numerical example. Finally, the validity of the stated method as well as a comparative analysis with the existing methods is addressed in detail.

An overlapping community detection algorithm based on node distance of line graph

2019 ◽  
Vol 33 (26) ◽  
pp. 1950322 ◽  
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Line Graph ◽  
Detection Algorithm ◽  
Density Peak ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Normalized Mutual Information ◽  
Overlapping Community ◽  
Community Detection Algorithm

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

2021 ◽  
Author(s):  
Yan Ma ◽  
Guoqiang Chen
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Monitoring Network ◽  
Simple Algorithm ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Density Peak ◽  
Propagation Algorithm ◽  
Clustering Center ◽  
Community Detection Algorithm

Abstract Community structure detection in complex network structure and function to understand network relations, found its evolution rule, monitoring and forecasting its evolution behavior has important theoretical significance, in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism and safeguard national security has wide application prospect. Label propagation algorithm is one of the popular algorithms for community detection in recent years, the community detection algorithm based on tags spread the biggest advantage is the simple algorithm logic, relative to the module of optimization algorithm convergence speed is very fast, the clustering process without any optimization function, and the initialization before do not need to specify the number of complex network community. However, the algorithm has some problems such as unstable partitioning results and strong randomness. To solve this problem, this paper proposes an unsupervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to find the clustering center, first determines the prototype of the community, and then fixes the number of communities and the clustering center of the complex network, and then uses the label propagation algorithm to detect the community, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the community. Finally, experiments on synthetic network and real network data sets are carried out with the proposed algorithm, and the results show that the proposed method has better performance.

scholarly journals A NEW SYNTHESIS METHOD OF STRUCTURAL, TECHNOLOGICAL AND SAFETY DECISIONS (SYMAD-3) / NAUJAS KONSTRUKCINIŲ, TECHNOLOGINIŲ IR SAUGOS SPRENDIMŲ SINTEZĖS METODAS (SYMAD-3)

2012 ◽  
Vol 18 (2) ◽  
pp. 265-276 ◽  
Author(s):  
Ruta Simanaviciene ◽  
Rita Liaudanskiene ◽  
Leonas Ustinovichius
Keyword(s):  
Decision Making ◽  
Synthesis Method ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Quantitative Measurements ◽  
New Synthesis ◽  
Multiple Attribute ◽  
Order Preference ◽  
Simple Additive Weighting ◽  
Topsis Technique

The paper provides a new synthesis method of multiple attribute decisions (SyMAD-3 – Synthesis of Multiple Attribute Decisions using three methods) intended for combining multi-stage and multiple attribute decisions into a single common estimate. The method is applied for selecting a construction project on the basis of its structural, technological and safety decisions. To increase the reliability of the decision, three multiple attribute decision-making methods based on quantitative measurements were applied to help the person making a decision to monitor the results of a relevant decision obtained employing three methods of the same class. The algorithm of the proposed method includes methods for identifying the integrated significances of attributes and multiple attribute decision-making methods (SAW – Simple Additive Weighting, TOPSIS – Technique for Order Preference by Similarity to Ideal Solution, and COPRAS – COmplex PRoportional ASsessment) based on quantitative measurements. Santrauka Šiame darbe autoriai pateikia naują daugiakriterinių sprendimų sintezės metodą (SyMAD-3 – Synthesis of Multiple Attribute Decisions using three methods), skirtą daugiapakopiams, daugiatiksliams sprendimams apjungti į vieną bendrą įvertį. Metodas taikomas statybos projektui parinkti atsižvelgiant į konstrukcinius, technologinius ir saugos sprendimus. Sprendimo patikimumui padidinti taikomi trys kiekybiniais matavimais pagrįsti daugiatiksliai sprendimo priėmimo metodai, kuriais remdamasis sprendimą priimantis asmuo gali stebėti jam aktualaus sprendimo rezultatus, gautus trimis metodais, priklausančiais tai pačiai klasei. Pateikto metodo algoritme taikomi efektyvumo rodiklių integruoto reikšmingumo nustatymo ir daugiatiksliai sprendimo priėmimo (SAW – Simple Additive Weighting, TOPSIS – Technique for Order Preference by Similarity to Ideal Solution, COPRAS – COmplex PRoportional ASsessment) metodai, pagrįsti kiekybiniais matavimais.

scholarly journals TOPSIS Method Based on Correlation Coefficient and Entropy Measure for Linguistic Pythagorean Fuzzy Sets and Its Application to Multiple Attribute Decision Making

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mingwei Lin ◽  
Chao Huang ◽  
Zeshui Xu
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Fuzzy Topsis ◽  
Multiple Attribute Decision Making ◽  
Entropy Measure ◽  
Security Evaluation ◽  
Topsis Method ◽  
Pythagorean Fuzzy Set ◽  
Multiple Attribute ◽  
Topsis Technique

The linguistic Pythagorean fuzzy set (LPFS) is an important implement for modeling the uncertain and imprecise information. In this paper, a novel TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method is proposed for LPFSs based on correlation coefficient and entropy measure. To this end, the correlation coefficient is proposed for the relationship measurement between LPFSs. Afterwards, two entropy measures are developed to calculate the attribute weight information. Then, a novel linguistic Pythagorean fuzzy TOPSIS (LPF-TOPSIS) method is proposed to solve multiple attribute decision-making problems. Finally, the LPF-TOPSIS method is applied to handle a case concerning the selection of firewall productions, and then, a case concerning the security evaluation of computer systems is given to conduct the comparative analysis between the proposed LPF-TOPSIS method and previous decision-making methods for validating the superiority of the proposed LPF-TOPSIS method.

Sign in / Sign up

Export Citation Format

Share Document