Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms.

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Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽

10.3390/e23060680 ◽

2021 ◽

Vol 23 (6) ◽

pp. 680

Author(s):

Hanyang Lin ◽

Yongzhao Zhan ◽

Zizheng Zhao ◽

Yuzhong Chen ◽

Chen Dong

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Adjustment Strategy ◽

Topology Information ◽

Overlapping Community ◽

Real World Datasets ◽

Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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An algorithm Walktrap-SPM for detecting overlapping community structure

International Journal of Modern Physics B ◽

10.1142/s0217979217501211 ◽

2017 ◽

Vol 31 (15) ◽

pp. 1750121 ◽

Cited By ~ 1

Author(s):

Fang Hu ◽

Youze Zhu ◽

Yuan Shi ◽

Jianchao Cai ◽

Luogeng Chen ◽

...

Keyword(s):

Community Structure ◽

Random Walk ◽

Community Detection ◽

Real World ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Running Time ◽

Overlapping Community ◽

Negative Links ◽

Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

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NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Sensors ◽

10.3390/s19020260 ◽

2019 ◽

Vol 19 (2) ◽

pp. 260 ◽

Cited By ~ 4

Author(s):

Bingyang Huang ◽

Chaokun Wang ◽

Binbin Wang

Keyword(s):

Community Detection ◽

Real World ◽

State Of The Art ◽

Label Propagation ◽

Detection Methods ◽

Overlapping Communities ◽

Propagation Algorithm ◽

Pruning Strategy ◽

Node Similarity ◽

Attributed Networks

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

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Community cores expansion for overlapping community detection in complex networks

Modern Physics Letters B ◽

10.1142/s0217984918504055 ◽

2018 ◽

Vol 32 (33) ◽

pp. 1850405 ◽

Cited By ~ 2

Author(s):

Yongjie Yan ◽

Guang Yu ◽

Xiangbin Yan ◽

Hui Xie

Keyword(s):

Complex Networks ◽

Community Detection ◽

Heuristic Algorithm ◽

Real World ◽

Local Structure ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Clique Percolation ◽

Overlapping Community ◽

Synthetic Datasets

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

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Genetic algorithm synthesis of four-bar mechanisms

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400001700 ◽

1996 ◽

Vol 10 (5) ◽

pp. 371-390 ◽

Cited By ~ 21

Author(s):

Gerald P. Roston ◽

Robert H. Sturges

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Classical Theory ◽

Real World ◽

Design Problem ◽

Numerical Technique ◽

Active Area ◽

Mechanism Synthesis ◽

Classical Design ◽

Real World Problems

AbstractThe synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.

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A Comparison between Memetic Algorithm and Genetic Algorithm for an Integrated Logistics Network with Flexible Delivery Path

SN Operations Research Forum ◽

10.1007/s43069-021-00087-8 ◽

2021 ◽

Vol 2 (3) ◽

Author(s):

Elham Behmanesh ◽

Jürgen Pannek

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Real World ◽

Memetic Algorithm ◽

Allocation Problem ◽

Strategic Decision ◽

Neighborhood Search ◽

Test Problems ◽

Chain Model ◽

Term Operation

AbstractThe distribution/allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution/allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solutions particularly for large-scale test problems. This paper presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a neighborhood search mechanism and novelty in population presentation method called “extended random path direct encoding method.” To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as comparison basis for small size problems. In large-size cases that we are dealing with in real world, a classical genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

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A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

Journal of Applied Mathematics ◽

10.1155/2013/214814 ◽

2013 ◽

Vol 2013 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Chih-Hao Lin ◽

Jiun-De He

Keyword(s):

Genetic Algorithm ◽

Real World ◽

Function Analysis ◽

Wide Spectrum ◽

Solution Space ◽

Objective Functions ◽

Routing Problems ◽

Local Optima ◽

Mutual Evaluation ◽

Real World Problems

Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.

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A Genetic Algorithm's Approach to the Optimization of Capacitated Vehicle Routing Problems

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch008 ◽

2016 ◽

pp. 209-235 ◽

Cited By ~ 3

Author(s):

Mariano Frutos ◽

Fernando Tohmé ◽

Fabio Miguel

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Real World ◽

Selective Pressure ◽

Fresh Fruit ◽

Vehicle Routing Problems ◽

Routing Problems ◽

The Family ◽

Capacitated Vehicle ◽

Real World Problems

This chapter addresses the family of problems known in the literature as Capacitated Vehicle Routing Problems (CVRP). A procedure is introduced for the optimization of a version of the generic CVRP. It generates feasible clusters and, in a first step, yields a coding of their ordering. The next stage provides this information to a genetic algorithm for its optimization. A selective pressure process is added in order to improve the selection and subsistence of the best candidates. This arrangement allows improving the performance of the algorithm. We test it using Van Breedam and Taillard's problems, yielding similar results as other algorithms in the literature. Besides, we test the algorithm on real-world problems, corresponding to an Argentinean company distributing fresh fruit. Four instances, with 50, 100, 150 and 200 clients were examined, giving better results than the current plans of the company.

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