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The Effect of Faults on Network Expansion
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The Effect of Faults on Network Expansion
Amitabha Bagchi1 , Ankur Bhargava2 , Amitabh Chaudhary3 , David Eppstein4 and Christian Scheideler5 
| (1) |
Department of Computer Science and Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India |
| (2) |
Google, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA |
| (3) |
Department of Computer Science and Engineering, Notre Dame University, Notre Dame, IN 46556, USA |
| (4) |
Department of Computer Science, University of California, Irvine, CA 92697, USA |
| (5) |
Insitut fur Informatik,Technische Universitat Munchen, Boltzmannstrasse 3, D-85748, Garching, Germany |
Received: 18 January 2006 Published online: 20 September 2006
Abstract We study the problem of how resilient networks are to node faults. Specifically, we investigate the question of how many faults
a network can sustain and still contain a large (i.e., linear-sized) connected component with approximately the same expansion
as the original fault-free network. We use a pruning technique that culls away those parts of the faulty network that have
poor expansion. The faults may occur at random or be caused by an adversary. Our techniques apply in either case. In the adversarial
setting we prove that for every network with expansion

a large connected component with basically the same expansion as the original network exists for up to a constant times

faults. We show this result is tight in the sense that every graph G of size n and uniform expansion

can be broken into components of size o(n) with

faults. Unlike the adversarial case, the expansion of a graph gives a very weak bound on its resilience to random faults.
While it is the case, as before, that there are networks of uniform expansion

that are not resilient against a fault probability of a constant times

it is also observed that there are networks of uniform expansion

that are resilient against a constant fault probability. Thus, we introduce a different parameter, called the span of a
graph, which gives us a more precise handle on the maximum fault probability. We use the span to show the first known results
for the effect of random faults on the expansion of d-dimensional meshes.
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