SIAM Journal on Applied Mathematics
Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
A guided tour of Chernoff bounds
Information Processing Letters
Adaptive broadcasting with faulty nodes
Parallel Computing
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
The power of two choices in randomized load balancing
The power of two choices in randomized load balancing
Sampling regular graphs and a peer-to-peer network
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On the communication complexity of randomized broadcasting in random-like graphs
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
Distributed random digraph transformations for peer-to-peer networks
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
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 the bit communication complexity of randomized rumor spreading
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
How efficient can gossip be? (on the cost of resilient information exchange)
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Efficient information exchange in the random phone-call model
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Communication complexity of quasirandom rumor spreading
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Efficient broadcasting in random power law networks
WG'10 Proceedings of the 36th international conference on Graph-theoretic concepts in computer science
Stabilizing consensus with the power of two choices
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Social networks spread rumors in sublogarithmic time
Proceedings of the forty-third annual ACM symposium on Theory of computing
Asymptotically optimal randomized rumor spreading
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Faster coupon collecting via replication with applications in gossiping
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
Randomised broadcasting: memory vs. randomness
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
On the randomness requirements of rumor spreading
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
The worst case behavior of randomized gossip
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
Experimental analysis of rumor spreading in social networks
MedAlg'12 Proceedings of the First Mediterranean conference on Design and Analysis of Algorithms
Randomised broadcasting: Memory vs. randomness
Theoretical Computer Science
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In this paper we analyze the runtime and number of message transmissions generated by simple randomized broadcasting algorithms in random-like networks, and show that an apparently minor change in the ability of the nodes implies an exponential decrease in the average communication overhead produced by these algorithms at an arbitrary node. A natural randomized broadcasting protocol, called random phone call model, has been introduced by Karp et al. [20]. In this model it is assumed that in each time step, every node of G calls a neighbor, chosen uniformly at random, and establishes a communication channel with this neighbor. Any node u is then allowed to send/receive messages to/from all nodes which have established communication channels with u in the current time step. Karp et al. showed that some piece of information r, placed initially on one of the nodes of a complete graph of size n, can be spread with probability 1 - n-Ω(1) to all nodes of this graph within O(log n) time steps, by using O(n log log n) transmissions related to r. Furthermore, they proved that this result is asymptotically optimal among all address-oblivious broadcasting algorithms. In a recent paper [9], we analyzed the random phone call model in traditional random graphs Gn,p with p log2 n/n, and showed that any address-oblivious broadcasting algorithm requires with high probability Ω(log n) time steps and Ω(n (log log n + log n/log(pn))) message transmissions to inform all nodes of such a graph. In this paper we consider two simple modifications of the random phone call model, and show that in both cases the number of total message transmissions can be reduced significantly (up to almost a logarithmic factor). In the first case, we allow each node of a random graph Gn,p with p logδ n/n, where λ is a properly chosen constant, to call in every time step four different neighbors, chosen uniformly at random, and we prove that the number of message transmissions decreases to O(n log log n). This can be viewed as a "power of multiple choices" type theorem for randomized broadcasting. Then we show that if in the random phone call model the nodes are provided with a little memory, i.e., they are able to remember the addresses of the nodes chosen in the most recent three time steps, then the communication overhead decreases substantially, too. Finally, we prove the optimality of our results. The algorithms presented in this paper can cope with restricted communication failures, only require rough estimates of the number of nodes, and are robust against slight topological changes. In addition, our results can be extended to the generalized random graph model of [6].