scholarly journals Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis

2018 ◽  
Vol 232 ◽  
pp. 01021
Author(s):  
Wangping Xiong ◽  
Xian Zhou ◽  
Bin Nie ◽  
Jianqiang Du
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Large Scale ◽  
Network Pharmacology ◽  
Network Community ◽  
Community Classification ◽  
Different Types ◽  
Modularity Index ◽  
New Algorithms

Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm.

Effective pollutant retention capacity of permeable pavements for infiltrated road runoffs determined by laboratory tests

2005 ◽  
Vol 51 (2) ◽  
pp. 37-45 ◽  
Author(s):  
S. Fach ◽  
W.F. Geiger
Keyword(s):  
Large Scale ◽  
Urban Runoff ◽  
Assessment Procedure ◽  
Retention Capacity ◽  
Test Installation ◽  
Permeable Pavements ◽  
Batch Tests ◽  
Different Types ◽  
Stormwater Infiltration

The infiltration of urban runoff always implies an entrance of pollutants into the soil and ground water. Due to legal regulations in many communes there is no longer any permission needed for stormwater infiltration, if administrative regulations and the requirements of standards are observed. The results of a research project carried out under the heading “Development of an assessment procedure for permeable pavements” show, that the pollutant retention capacity of permeable pavements varies considerably, depending on the material and the specific reactive surface. The objective of the study was to work out recommendations of suitable permeable pavements for different types of urban runoff. Selected data about the quality of urban runoff was compiled into a runoff matrix, which was used for defining characteristic dilutions. In batch tests, the material of the infiltration devices is penetrated with the dilutions. A test installation in large scale is used to calibrate the sorption coefficients derived from the batch experiment.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

scholarly journals Improving the Red Color and Fruit Quality of ‘Kent’ Mango Fruit by Pruning and Preharvest Spraying of Prohydrojasmon or Abscisic Acid

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 944
Author(s):  
Sudheeran Pradeep Kumar ◽  
Dalia Maurer ◽  
Oleg Feygenberg ◽  
Cliff Love ◽  
Noam Alkan
Keyword(s):  
Abscisic Acid ◽  
Large Scale ◽  
Tree Canopy ◽  
Positive Effects ◽  
Color Development ◽  
Air Jet ◽  
Red Color ◽  
Different Types ◽  
Pruning Techniques

Pre-harvest application of prohydrojasmon (PDJ) or abscisic acid (ABA) induces the red color in fruits that were exposed to sunlight at the orchard. In this large-scale work, we evaluated the effect of two different pruning techniques of ‘Kent’ mango orchards, one leading to opening the orchard canopy to expose as much fruit as possible to sunlight, while the second pruning leads to square-shaped trees and subsequently reduces the amount of sunlight reaching the fruit. These two pruning methods were combined with preharvest spraying with prohydrojasmon (PDJ) or abscisic acid (ABA) using two different types of sprayers, i.e., regular and air-jet sprayer. Pruning the canopy of the orchards to open and closed trees exposed 80% or 30% of fruits to sunlight, respectively. Both of the application with air-jet and regular sprayers effectively covered the fruit without causing fruit detachment and damage to yield. Both the phytohormones (PDJ and ABA) application treatments induced red blush skin, red intensity, anthocyanin, and flavonoids, particularly in fruit grown outside the tree canopy in both open and closed trees. PDJ and ABA treatments exhibited marginally reduced acidity than the untreated control, while the brix was not affected much by any of the treatments. Besides these, exposure to sunlight and PDJ treatment also reduced postharvest decay and increased chlorophyll degradation and yellowing in comparison to the controls. This study promoted applicative evidence about the positive effects of exposure to sunlight, prohydrojasmon (PDJ), and abscisic acid (ABA) on red color development without compromising the mango fruit’s quality.

scholarly journals A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019 ◽  
Vol 8 (6) ◽  
pp. 933-937
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Linear Time ◽  
A Priori ◽  
Label Propagation ◽  
Data Sets ◽  
Network Graph ◽  
Priori Information

Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

scholarly journals Research on Community Detection in Complex Networks Based on Internode Attraction

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1383
Author(s):  
Jinfang Sheng ◽  
Cheng Liu ◽  
Long Chen ◽  
Bin Wang ◽  
Junkai Zhang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Rapid Development ◽  
Structure Detection ◽  
Detection Approach ◽  
Real World Datasets ◽  
New Community ◽  
Large Scale Networks

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

Complex networks, community structure, and catchment classification in a large-scale river basin

2017 ◽  
Vol 545 ◽  
pp. 478-493 ◽  
Author(s):  
Koren Fang ◽  
Bellie Sivakumar ◽  
Fitsum M. Woldemeskel
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
River Basin ◽  
Large Scale

Uncovering the fuzzy community structure accurately based on steepest descent projection

2017 ◽  
Vol 31 (27) ◽  
pp. 1750249 ◽  
Author(s):  
Changjian Fang ◽  
Dejun Mu ◽  
Zhenghong Deng ◽  
Jiaqi Yan
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Large Scale ◽  
Steepest Descent ◽  
Topological Information ◽  
Generalized Framework ◽  
Open Question ◽  
Fuzzy C Means Algorithm

Uncovering the community structure in complex network is a hot research point in recent years. How to identify the community structure accurately in complex network is still an open question under research. There are lots of methods based on topological information, which have some good performances at the expense of longer runtimes. In this paper, we propose a new fuzzy algorithm which follows the line of fuzzy c-means algorithm. A steepest descent framework with projection by optimizing the quality function is presented under the generalized framework. The results of experiments on both real-world networks and synthetic networks show that the proposed method achieves the highest efficiency and is easy for detecting fuzzy community structure in large-scale complex networks.

scholarly journals Determining Community Structure and Modularity in Social Network using Genetic Algorithm

2020 ◽  
Vol 14 (3) ◽  
pp. 219
Author(s):  
Taufan Bagus Dwi Putra Aditama ◽  
Azhari SN
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Bottlenose Dolphins ◽  
College Football ◽  
New Algorithms

 Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as email networks and social networks. The popularity determines the structure of a community because it can analyze the structure.Meanwhile, to determine the structure of the community by maximizing the value of modularity is difficult. Therefore, a lot of research introduces new algorithms to solve problems in determining community structure and maximizing the value of modularity. Genetic Algorithm can provide effective solutions by combining exploration and exploitation.This study focuses on the Genetic Algorithm which added a cleanup feature in the process. The final results of this study are the results of a comparison of modularity values based on the determination of the community structure of the Genetic Algorithm, Girvan and Newman Algorithm, and the Louvain Algorithm. The best modularity values were obtained using the Genetic Algorithm which obtained 0.6833 results for Zachary's karate club dataset, 0.7446 for the Bottlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset.

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