SIAM Journal on Applied Mathematics
A guided tour of Chernoff bounds
Information Processing Letters
Randomized Broadcast in Networks
SIGAL '90 Proceedings of the International Symposium on Algorithms
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Search in JXTA and Other Distributed Networks
P2P '01 Proceedings of the First International Conference on Peer-to-Peer Computing
On the communication complexity of randomized broadcasting in random-like graphs
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)
Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics)
The power of memory in randomized broadcasting
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
On mixing and edge expansion properties in randomized broadcasting
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
On randomized broadcasting in power law networks
DISC'06 Proceedings of the 20th international conference on Distributed Computing
On the runtime and robustness of randomized broadcasting
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Randomised broadcasting: Memory vs. randomness
Theoretical Computer Science
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We consider broadcasting in random power law graphs by using a simple modification of the so-called random phone call model introduced by Karp, Schindelhauer, Shenker, and Vöcking (FOCS 2000). In the phone call model, every time step each node calls on a randomly chosen neighbor, and establishes a communication channel to this node. The communication channels can then be used to transmit messages in both directions. We show that if we allow every node to choose ρ neighbors instead of one, where ρ is some constant, then the average number of message transmissions per node decreases exponentially in certain random power law graphs. Formally, we present an algorithm that completes broadcasting in time O(log n) and uses O(n log log n) transmissions per message, with probability 1-n-Ω(1), where n is the size of the underlying network.