Journal of Combinatorial Optimization

, Volume 30, Issue 3, pp 747–767 | Cite as

A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks

  • Thang N. Dinh
  • Nam P. Nguyen
  • Md Abdul Alim
  • My T. Thai


We introduce A\(^3\)CS, an adaptive framework with approximation guarantees for quickly identifying community structure in dynamic networks via maximizing Modularity Q. Our framework explores the advantages of the power-law distribution property found in many real-world complex systems. The framework is scalable for very large networks, and more excitingly, possesses approximation factors to ensure the quality of its detected community structure. To the best of our knowledge, this is the first framework that achieves approximation guarantees for the NP-hard Modularity maximization problem, especially on dynamic scale-free networks. To certify our approach, we conduct extensive experiments in comparison with other adaptive methods on both synthesized networks with known community structures and real-world traces including ArXiv e-print citation and Facebook social networks. Excellent empirical results not only confirm our theoretical results but also promise the practical applicability of A\(^3\)CS in a wide range of dynamic networks.


Adaptive approximation algorithm Community structure  Modularity Social networks 



This work is partially supported by NSF CAREER AWARD 0953284 and HDTRA-1-10-1-0050.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Thang N. Dinh
    • 1
  • Nam P. Nguyen
    • 1
    • 2
  • Md Abdul Alim
    • 1
  • My T. Thai
    • 1
  1. 1.Deparment of Computer and Information Science and EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Computer and Information Sciences DepartmentTowson UniversityTowsonUSA

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