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
Randomized algorithms
Adaptive broadcasting with faulty nodes
Parallel Computing
The Mathematics of Infectious Diseases
SIAM Review
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Dissemination of Information in Communication Networks: Broadcasting, Gossiping, Leader Election, and Fault-Tolerance (Texts in Theoretical Computer Science. An EATCS Series)
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
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
The power of memory in randomized broadcasting
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
On the fault tolerance of some popular bounded-degree networks
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Broadcasting vs. mixing and information dissemination on Cayley graphs
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
On the runtime and robustness of randomized broadcasting
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Gossiping (via mobile?) in social networks
Proceedings of the fifth international workshop on Foundations of mobile computing
Quasirandom Rumor Spreading: Expanders, Push vs. Pull, and Robustness
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Almost tight bounds for rumour spreading with conductance
Proceedings of the forty-second ACM symposium on Theory of computing
Rumour spreading and graph conductance
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Efficient broadcasting in random power law networks
WG'10 Proceedings of the 36th international conference on Graph-theoretic concepts in computer science
Asymptotically optimal randomized rumor spreading
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
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
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A very simple and natural broadcasting algorithm is the so-called push algorithm which has several applications in the area of distributed computing. Initially, only one vertex of a graph G = (V,E) owns a piece of information which is spread iteratively to all other vertices: in each time step t = 1, 2, ... every informed vertex chooses some neighbor uniformly at random which then becomes informed and may itself inform other vertices in the succeeding time steps. The crucial question is how many time steps are required such that all vertices become informed (with high probability). For various graph classes, involved methods have been developed in order to show an upper bound of O(logN + diam(G)), where N is the number of vertices and diam(G) denotes the diameter of G. However, currently no asymptotically tight bound on the runtime of the push algorithm based on the mixing time exists. In this work we fill this gap by deriving an upper bound of O(logN + Tmix), where Tmix denotes the mixing time of a certain random walk on G. After that we give a simple but useful upper bound which is based on a certain average value of the edge expansion of G. Unfortunately, both approaches do not give the right bound for Hypercubes. Therefore, we develop a general way to combine them and prove that the runtime of the push algorithm is Θ(log N) on every Hamming graph.