A Connectivity Reduction Strategy for Multi-agent Systems

Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)


This paper considers the connectivity reduction of multi-agent systems which are represented with directed graphs. A simple distributed algorithm is presented for each agent to independently remove some of its incoming links based on only the local information of its neighbors. The algorithm results in an information graph with sparser connections. The goal is to reduce computational effort associated with communication while still maintaining overall system performance. The main contribution of this paper is a distributed algorithm that can, under certain conditions, find and remove redundant edges in a directed graph using only local information.


Directed Graph Transmission Range Multiagent System Communication Graph Triangle Closure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aho, A., Garey, M., Ullman, J.: The transitive reduction of a directed graph. SIAM J. Comput. 1(2), 131–137 (1972) MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Jadbabaie, A., Lin, J., Morse, A.S.: Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(9), 988–1001 (2003) CrossRefGoogle Scholar
  3. 3.
    Moyles, D.M., Thompson, G.L.: An algorithm for finding a minimum equivalent graph of a digraph. J. Assoc. Comput. Mach. 16(3), 455–460 (1969) MATHGoogle Scholar
  4. 4.
    Spanos, D.P., Murray, R.M.: Robust connectivity of networked vehicles. In: 43rd IEEE Conference on Decision and Control, pp. 2893–2898 (2004) Google Scholar
  5. 5.
    Lafferriere, G., Williams, A., Caughman, J., Veerman, J.J.P.: Decentralized control of vehicle formations. Syst. Control Lett. 54, 899–910 (2005) MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Fiedler, M.: Algebraic connectivity of graphs. Czechoslov. Math. J. 23(98), 298–305 (1973) MathSciNetGoogle Scholar
  7. 7.
    Ji, M., Egerstedt, M.: Distributed formation control while preserving connectedness. In: 45th IEEE Conference on Decision and Control, pp. 5962–5967 (2006) Google Scholar
  8. 8.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007) CrossRefGoogle Scholar
  9. 9.
    Khuller, S., Raghavachari, B., Young, N.: Approximating the minimum equivalent digraph. SIAM J. Comput. 24(4), 859–872 (1995) MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Ren, W., Beard, R.W.: Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Trans. Autom. Control 50(5), 655–661 (2005) CrossRefMathSciNetGoogle Scholar
  11. 11.
    Ren, W., Beard, R.W., Atkins, E.M.: A survey of consensus problems in multi-agent coordination. In: Proceedings of American Control Conference, pp. 1859–1864 (2005) Google Scholar
  12. 12.
    Kim, Y., Mesbahi, M.: On maximizing the second smallest eigenvalue of a state-dependent graph Laplacian. IEEE Trans. Autom. Control 116–120 (2006) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.California State UniversityNorthridgeUSA
  2. 2.Air Force Research LaboratoryEglin AFBUSA

Personalised recommendations