Fast Collaborative Graph Exploration

  • Dariusz Dereniowski
  • Yann Disser
  • Adrian Kosowski
  • Dominik Pająk
  • Przemysław Uznański
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7966)


We study the following scenario of online graph exploration. A team of k agents is initially located at a distinguished vertex r of an undirected graph. At every time step, each agent can traverse an edge of the graph. All vertices have unique identifiers, and upon entering a vertex, an agent obtains the list of identifiers of all its neighbors. We ask how many time steps are required to complete exploration, i.e., to make sure that every vertex has been visited by some agent.

We consider two communication models: one in which all agents have global knowledge of the state of the exploration, and one in which agents may only exchange information when simultaneously located at the same vertex. As our main result, we provide the first strategy which performs exploration of a graph with n vertices at a distance of at most D from r in time O(D), using a team of agents of polynomial size k = D n 1 + ε  < n 2 + ε , for any ε > 0. Our strategy works in the local communication model, without knowledge of global parameters such as n or D.

We also obtain almost-tight bounds on the asymptotic relation between exploration time and team size, for large k. For any constant c > 1, we show that in the global communication model, a team of k = D n c agents can always complete exploration in \(D(1+ \frac{1}{c-1} +o(1))\) time steps, whereas at least \(D(1+ \frac{1}{c} -o(1))\) steps are sometimes required. In the local communication model, \(D(1+ \frac{2}{c-1} +o(1))\) steps always suffice to complete exploration, and at least \(D(1+ \frac{2}{c} -o(1))\) steps are sometimes required. This shows a clear separation between the global and local communication models.


Competitive Ratio Communication Model General Graph Global Communication Exploration Strategy 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dariusz Dereniowski
    • 1
  • Yann Disser
    • 2
  • Adrian Kosowski
    • 3
  • Dominik Pająk
    • 3
  • Przemysław Uznański
    • 3
  1. 1.Gdańsk University of TechnologyPoland
  2. 2.TU BerlinGermany
  3. 3.CEPAGE projectInria Bordeaux Sud-OuestFrance

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