A survey on network community detection based on evolutionary computation

@article{Cai2016ASO,
  title={A survey on network community detection based on evolutionary computation},
  author={Qing Cai and Lijia Ma and Maoguo Gong and Dayong Tian},
  journal={Int. J. Bio Inspired Comput.},
  year={2016},
  volume={8},
  pages={84-98}
}
Uncovering community structures of a complex network can help us to understand how the network functions. Over the past few decades, network community detection has attracted growing research interest from many fields. Many community detection methods have been developed. Network community structure detection can be modelled as optimisation problems. Due to their inherent complexity, these problems often cannot be well solved by traditional optimisation methods. For this reason, evolutionary… 

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References

SHOWING 1-10 OF 129 REFERENCES

Community detection in networks by using multiobjective evolutionary algorithm with decomposition

Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering

This paper presents an approach for the problem of community detection using genetic algorithm (GA) in conjunction with the method of clustering, and demonstrates that the algorithms are highly effective at discovering community structure in both computer-generated and real-world network data.

A Novel Genetic Algorithm for Overlapping Community Detection

A novel algorithm to discover overlapping communities based on edge clustering is proposed, which is different from conventional algorithms based on node clustering and will discover groups of edges that have the same characteristics.

Fitness evaluation for overlapping community detection in complex networks

This paper investigates the performance of evolutionary algorithms for the task of detecting overlapping communities and shows that none of the fitness functions used are able to guide the search process towards good partitions based on a measure of the normalized mutual information.

Community Detection Using Cooperative Co-evolutionary Differential Evolution

This paper introduces Cooperative Co-evolution framework for detecting communities in complex networks, adopt Differential Evolution (DE) to optimize network modularity to search for an optimal partition of a network and design a novel mutation operator specifically for community detection.

A non-dominated neighbor immune algorithm for community detection in networks

This paper proposes a multi-objective approach, named NNIA-Net, to discover communities in networks by employing Non-dominated Neighbor Immune Algorithm (NNIA), and demonstrates that the algorithm is highly efficient at discovering quality community structure in both synthetic and real-world network data.

Multi-objective community detection in complex networks

GA-Net: A Genetic Algorithm for Community Detection in Social Networks

A genetic based approach to discover communities in social networks by optimizing a simple but efficacious fitness function able to identify densely connected groups of nodes with sparse connections between groups.

Overlapped community detection in complex networks

A genetic based approach to discover overlapping communities by optimizing a fitness function able to identify densely connected groups of nodes by employing it on the line graph corresponding to the graph modelling the network.

A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

A novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm and a local search operator was designed, which can improve the effectiveness and efficiency of community detection.
...