Combinatorica
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
Local expansion of vertex-transitive graphs and random generation in finite groups
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Broadcasting on recursively decomposable Cayley graphs
Proceedings of the international workshop on Broadcasting and gossiping 1990
An Optimal Broadcasting Algorithm without Message Redundancy in Star Graphs
IEEE Transactions on Parallel and Distributed Systems
Randomized algorithms
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE 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)
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
The Effect of Faults on Network Expansion
Theory of Computing Systems
Testing Expansion in Bounded-Degree Graphs
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Graph sparsification by effective resistances
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Rumor Spreading in Social Networks
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
Quasirandom Rumor Spreading: Expanders, Push vs. Pull, and Robustness
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
On Mixing and Edge Expansion Properties in Randomized Broadcasting
Algorithmica - Special Issue: Algorithms and Computation; Guest Editor: Takeshi Tokuyama
Broadcasting vs. mixing and information dissemination on Cayley graphs
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
Almost tight bounds for rumour spreading with conductance
Proceedings of the forty-second ACM symposium on Theory of computing
Reliable broadcasting in random networks and the effect of density
INFOCOM'10 Proceedings of the 29th conference on Information communications
Rumour spreading and graph conductance
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Fast Distributed Algorithms for Computing Separable Functions
IEEE Transactions on Information Theory
Rumor spreading and vertex expansion
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
The worst case behavior of randomized gossip
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
Strong robustness of randomized rumor spreading protocols
Discrete Applied Mathematics
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We study the relation between the vertex expansion of a graph and the performance of randomized rumor spreading (push model). We prove that randomized rumor spreading takes O((1/α) · polylog(n)) time on any regular n-vertex graph with vertex expansion α. This bound extends previously known upper bounds by replacing conductance by vertex expansion. Our result is almost tight in the sense that the dependency on (1/α) is optimal (up to logarithmic factors) and that on non-regular graphs with constant vertex expansion, the runtime can be polynomial in n. Our upper bound also implies that randomized rumor spreading is "fast" on every vertex-transitive graph and yields a new upper bound on the cover time of random walks. We also exhibit a subtle difference between the impact of vertex expansion and conductance on rumor spreading. We show that there are regular graphs with constant vertex expansion for which randomized rumor spreading takes considerably longer than on any regular graph with constant conductance. Finally, we also prove a more general, but weaker result for the push & pull model which also covers non-regular graphs.