A Multiobjective Genetic Algorithm to Find Communities in Complex Networks

By Clara Pizzuti. IEEE Transactions on Evolutionary Computation, 2012

323 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4543c208b204f1dc3e87393566c97429ac8c2989

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Kay Chen Tan
6. Cheng He
7. Langchun Si
8. Maoguo Gong

In Statistical and Nonlinear Physics

1. Maoguo Gong
2. Qing Cai
3. Ye Tian
4. Kay Chen Tan
5. Xiaowei Chen
6. Cheng He
7. Yaochu Jin
8. Lijia Ma

Citations in the corpus

• Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “To solve this problem, some MOEAs have been tailored for community detection by maximizing the intra-link density in all communities and minimizing the inter-link density between different communities [53, 132, 205].”
• Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, methodology): “In this paper, five EA-based algorithms named as GAnet , Meme-net, MOGA-net, MOCD, MOEA/D-net, and two MOPSO-based algorithms referred to as MOPSO-r1 and MOPSO-r2 are chosen to compare with the proposed approach .”