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A Michigan memetic algorithm for solving the community detection problem in complex network

NEUROCOMPUTING 2016年 214卷 535-545页

作者： Mirsaleh, Mehdi Rezapoor Meybodi, Mohammad Reza Amirkabir Univ Technol Comp Engn & Informat Technol Dept Soft Comp Lab Tehran Iran

community structure is an important feature in complex networks which has great significant for organization of networks. The community detection is the process of partitioning the network into some communities in such a way that there exist many connections in the communities and few connections between them. In this paper a Michigan memetic algorithm;called MLAMA-Net;is proposed for solving the community detection problem. The proposed algorithm is an evolutionary algorithm in which each chromosome represents a part of the solution and the whole population represents the solution. In the proposed algorithm, the population of chromosomes is a network of chromosomes which is isomorphic to the input network. Each node has a chromosome and a learning automaton (LA). The chromosome represents the community of corresponding node and saves the histories of exploration. The learning automaton represents a meme and saves the histories of the exploitation. The proposed algorithm is a distributed algorithm in which each chromosome locally evolves by evolutionary operators and improves by a local search. By interacting with both the evolutionary operators and local search, our algorithm effectively detects the community structure in complex networks and solves the resolution limit problem of modularity optimization. To show the superiority of our proposed algorithm over the some well-known algorithms, several computer experiments have been conducted. The obtained results show MLAMA-Net is effective and efficient at detecting the community structure in complex networks. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Complex networks community detection problem Learning automata (LA) Memetic algorithm (MA)

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IAFCO: an intelligent agent-based framework for combinatorial optimization

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JOURNAL OF SUPERCOMPUTING 2024年第8期80卷 10863-10930页

作者： Tajgardan, Mahjoubeh Izadkhah, Habib Lotfi, Shahriar Univ Tabriz Fac Math Stat & Comp Sci Tabriz Iran

Solving combinatorial optimization problems (COPs) poses a significant challenge in various application domains. The NP-hardness of many COPs necessitates the integration of meta-heuristics to effectively tackle these problems by leveraging the strengths of each meta-heuristic. However, hybrid meta-heuristics lack a mechanism to determine when to activate specific techniques during the search process. To address these limitations and enhance the overall generality, this study proposes a novel agent-based hyper-heuristic framework, referred to as IAFCO, for solving COPs. The proposed framework imbues the search strategy with greater intelligence and information, resulting in improved performance. The proposed framework comprises 4 coalitions that leverage different search perspectives to enhance the search process: a global coalition, a local coalition, a learning coalition, and a perturbation coalition. Additionally, a control agent is introduced to coordinate the coalitions. Using a reinforcement learning mechanism, the control agent autonomously selects the most suitable coalition of agents based on the search state. The set of states employed in this study is problem-independent and encompasses all possible states that may arise during the search process. Furthermore, some agents adopt chaotic numbers instead of random numbers to maintain diversity. To showcase the effectiveness of the proposed framework, its performance is evaluated on the software modularization problem (SMP) -also known as software clustering- and community detection problem (CDP) in complex networks as 2 case studies. The experiments on SMP's software systems, including 10 applications and 10 folders of Mozilla Firefox, and comparison with 21 modularization algorithms and experiments on CDP's networks, including 9 real-world networks, and comparison with 22 state-of-the-art community detection algorithms, indicate that the proposed framework is comparable to other existing methods. The source

关键词： Combinatorial optimization Hybrid meta-heuristics Agent-based framework Hyper-heuristic Search perspectives Reinforcement learning Software modularization problem (Software clustering) community detection problem

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Social Network Analysis: Beyond the Greediness in community detection Methods

Social Network Analysis: Beyond the Greediness in Community ...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Hernandez, Rodrigo Gutierrez, Inmaculada Castro, Javier Univ Complutense Madrid Madrid ES Spain

ISBN: (纸本)9783031671944;9783031671951

In the context of social network analysis, one of the main challenges concerns community detection. It has attracted the interest and significant attention towards contemporary multidisciplinary research in order to understand the complex structure of networks, unraveling clusters or communities within various interconnected systems. The Louvain algorithm has gained widespread popularity due to its remarkable calculation speed and efficiency, making it one of the most used methods. Although it is celebrated for its efficiency, its greedy nature poses certain challenges, especially in scenarios involving sparsely connected communities. Our study proposes an innovative method that enhances the performance of the Louvain algorithm without increasing its computational complexity. We explore the core applications of this method, seeking to refine and optimize the Louvain algorithm's performance. By addressing its weaknesses, particularly in handling low-connected communities, our research contributes to developing an improved version of this widely used algorithm. This enhancement is poised to broaden the algorithm's applicability and reliability, further cementing its status in the field of community detection within complex networks.

关键词： Social Networks Analysis Complex Networks community detection problem Greedy Algorithm

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Fuzzy Sugeno λ-measures and theirs applications to community detection problems

Fuzzy Sugeno λ-measures and theirs applications to communit...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Gutierrez, Inmaculada Gomez, Daniel Castro, Javier Espinola, Rosa Univ Complutense Madrid Fac Stat Madrid Spain Univ Complutense Madrid Inst Evaluac Sanitaria Fac Stat Madrid Spain

ISBN: (纸本)9781728169323

In this paper we propose a new framework for community detection problems. The starting point is a n-vector which defines some evidence about the elements of a finite set. This vector is used to build an interaction measure between the n elements of the set to which it refers. This interaction measure is represented by a Sugeno lambda-measure to which we make it being also a fuzzy measure. Then, we obtain the weighted graph associated with this new capacity measure. To carry on with it, we make use of the Shapley value. We also introduce the notion of extended vector fuzzy graph, which relates a graph with the capacity measure introduced in this work. Finally, we use a community detection method, based on Louvain algorithm, to search a cluster structure in the weighted graph. This partition is based on the relations among the individuals obtained from the initial vector. Let us note that in the case that there exist some connections among the elements, apart from their affinity, we can combine this extra information with that given by the vector, in order to obtain groups with highly-knit elements among which there are strong relations.

关键词： Fuzzy measure Sugeno lambda-measure community detection problem Extended vector fuzzy graph

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A variant of EAM to uncover community structure in complex networks

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2020年第2期16卷 102-110页

作者： Singh, Tribhuvan Mishra, Krishn Kumar Ranvijay MNNIT Allahabad Dept Comp Sci & Engn Prayagraj India

Environmental adaptation method (EAM) was developed to solve single-objective optimisation problems. After the first proposal, other variants have been suggested to speed up the convergence rate and to maintain the diversity of the solutions. Among those variants, IEAM-RP works with real numbers. In this paper, a variant of IEAM-RP has been suggested with major changes in adaptation operator to improve the overall performance of the algorithm. In the proposed method, significant attention has been given for balancing exploration and exploitation of individuals in the population. The performance of the proposed algorithm is compared against 14 state-of-the-art algorithms using standard benchmark functions of the comparing continuous optimisers (COCO) framework. Further, to check the effectiveness of the proposed approach, it has been applied to a real-world problem of community detection in complex networks. Again, the experimental results are found very promising and competitive compared to other algorithms.

关键词： single-objective optimisation evolutionary algorithms environmental adaptation method EAM community detection problem

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GA-LP: A genetic algorithm based on Label Propagation to detect communities in directed networks

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 74卷 127-138页

作者： Francisquini, Rodrigo Rosset, Valerio Nascimento, Maria C. V. Univ Fed Sao Paulo UNIFESP Inst Ciencia & Tecnol Av Cesare MG Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil

Many real-world networks have a topological structure characterized by cohesive groups of vertices. To perform the task of identifying such subsets of vertices, community detection in networks has aroused the interest of researchers and practitioners alike. In spite of the existence of various efficient community detection algorithms in the literature, most of them uses global information about the network, not applicable to distributed networks. This paper proposes a genetic-based algorithm to detect communities in directed networks based on local information to generate the offspring. The major difference between the proposed strategy and those found in the literature is the way of exploiting target regions of interest in the solution space. This step is directly influenced by the crossover operator that depends largely on the individual representation. In the introduced strategy, GA-LP, the individual is locally stored in the vertices as labels, what brings more flexibility in the system to be adapted to address applications that involve, for example, dynamic networks. In computational experiments, the proposed strategy showed an outstanding performance, being fast, achieving the best results on average in the networks tested. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Label Propagation Genetic algorithm community detection problem

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