A Concentration-based Artificial Immune Network for combinatorial optimization

Coelho, Guilherme Palermo; França, Fabrício Olivetti de; Zuben, Fernando J. Von

doi:10.1109/cec.2011.5949758

Cited by 21 publications

(51 citation statements)

References 7 publications

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“…As most of the adaptations proposed in [5] were adopted here as well, only a brief explanation of the general aspects of cob-aiNet[C] will be presented here, together with details about those aspects that differ from the adaptation proposed in [5]. For further details, the reader is referred to [12,5].…”

Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

“…The cob-aiNet[C] algorithm, which was originally proposed to solve combinatorial optimization problems [12], was previously adapted to identify both disjoint and overlapping communities in complex networks [5]. As most of the adaptations proposed in [5] were adopted here as well, only a brief explanation of the general aspects of cob-aiNet[C] will be presented here, together with details about those aspects that differ from the adaptation proposed in [5].…”

Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

“…As previously mentioned, an adaptation of the cob-aiNet[C] algorithm (Concentration-based Artificial Immune Network for Combinatorial Optimization - [12]) was adopted in this paper to obtain a set of communities for complex networks that maximize the new proposed quality function (Flex). The cob-aiNet[C] algorithm, which was originally proposed to solve combinatorial optimization problems [12], was previously adapted to identify both disjoint and overlapping communities in complex networks [5].…”

Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

“…Flex was combined with an adapted version of the immune-inspired optimization algorithm named cob-aiNet[C] [12] and applied to identify (both disjoint and overlapping) communities in a set of eight artificial and four realworld complex networks. The obtained results have shown that the partitions obtained with the optimization of this new metric are more coherent with the known real partitions than those obtained with the optimization of modularity.…”

Section: Introductionmentioning

confidence: 99%

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A Flexible Fitness Function for Community Detection in Complex Networks

França

Coelho

2015

Studies in Computational Intelligence

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Abstract. Most community detection algorithms from the literature work as optimization tools that minimize a given quality (or fitness) function, while assuming that each node belongs to a single community. Although several studies propose fitness functions for the detection of communities, the definition of what a community is is still vague. Therefore, each proposal of fitness function leads to communities that reflect the particular definition of community adopted by the authors. Besides, such communities not always correspond to the real partition observed in practice. This paper proposes a new flexible fitness function for community detection that allows the user to obtain communities that reflect distinct characteristics according to what is needed. This new fitness function was combined with an adapted version of the immune-inspired optimization algorithm named cob-aiNet[C] and applied to identify (both disjoint and overlapping) communities in a set of artificial and real-world complex networks. The results have shown that the partitions obtained with the optimization of this new metric are more coherent (when compared to the real, known, partitions) than those obtained with one of the most adopted function from the literature: modularity.

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Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

Section: The Cob-ainet[c] Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Flexible Fitness Function for Community Detection in Complex Networks

França

Coelho

2015

Studies in Computational Intelligence

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“…In this context, it is possible to highlight the family of algorithms based on the Artificial Immune Network paradigm [9], [10], [11], [12], particularly cob-aiNet (Concentration-based Artificial Immune Network for Continuous Optimization) and its variations [13], [14], which were successfully applied to several multimodal problems with distinct properties.…”

Section: Introductionmentioning

confidence: 99%

Identifying overlapping communities in complex networks with multimodal optimization

França

Coelho

2013

2013 IEEE Congress on Evolutionary Computation

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The analysis of complex networks is an important research topic that helps us understand the underlying behavior of complex systems and the interactions of their components. One particularly relevant analysis is the detection of communities formed by such interactions. Most community detection algorithms work as optimization tools that minimize a given quality function, while assuming that each node belongs to a single community. However, most complex networks contain nodes that belong to two or more communities, which are called bridges. The identification of bridges is crucial to several problems, as they often play important roles in the system described by the network. By exploiting the multimodality of quality functions, it is possible to obtain distinct optimal communities where, in each solution, each bridge node belongs to a distinct community. This paper proposes a technique that tries to identify a set of (possibly) overlapping communities by combining diverse solutions contained in a pool, which correspond to disjoint community partitions of a given network. To obtain the pool of partitions, an adapted version of the immune-inspired algorithm named cob-aiNet[C] was adopted here. The proposed methodology was applied to four real-world social networks and the obtained results were compared to those reported in the literature. The comparisons have shown that the proposed approach is competitive and even capable of overcoming the best results reported for some of the problems.

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