Cooperative co-evolutionary module identification with application to cancer disease module discovery

Research output: Contribution to journalArticlepeer-review


  • Guanbo Jia
  • Zexuan Zhu
  • Qiang Huang
  • Ke Tang
  • Jing Liu

Colleges, School and Institutes


Module identification or community detection in complex networks has become increasingly important in many scientific fields because it provides insight into the relationship and interaction between network function and topology. In recent years, module identification algorithms based on stochastic optimization algorithms such as Evolutionary Algorithms have been demonstrated to be superior to other algorithms on small to medium scale networks. However, the scalability and resolution limit problems of these module identification algorithms have not been fully addressed, which impeded their application to real-world networks. This paper proposes a novel module identification algorithm called Cooperative Co-evolutionary Module Identification to address these two problems. The proposed algorithm employs a cooperative co-evolutionary framework to handle large scale networks. We also incorporate a recursive partitioning scheme into the algorithm to effectively address the resolution limit problem. The performance of our algorithm is evaluated on twelve benchmark complex networks. As a medical application, we apply our algorithm to identify disease modules that differentiate low and high grade glioma tumours to gain insights into the molecular mechanisms that underpin the progression of glioma. Experimental results show that the proposed algorithm has a very competitive performance compared with other state-of-the-art module identification algorithms.


Original languageEnglish
Pages (from-to)874-891
Number of pages18
JournalIEEE Transactions on Evolutionary Computation
Issue number6
Early online date29 Feb 2016
Publication statusPublished - 1 Dec 2016