Spatial gossip and resource location protocols
By David Kempe, Jon M. Kleinberg, A. Demers. Symposium on the Theory of Computing, 2001
317 citations recorded by the source corpus.
Subfields: Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/1ff04c1e775a4f6cd009a359fbf7516947c15979
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Jon M. Kleinberg
- Supratim Deb
- Clifford Choute
- K. Birman
- Indranil Gupta
- Muriel Médard
- B.D.O. Anderson
- Robbert van Renesse
Citations in the corpus
- Algebraic gossip: a network coding approach to optimal multiple rumor mongering (2006, background): “In recent work, [13], [12] considered the scenario where there are multiple messages, each with a unique destination.”
- Clique Gossiping (2017, background): “2952082 nodes gossip by exchanging information between each other as a fundamental resource allocation protocol for computer networks [9], [10].”
- Co-operative downloading in vehicular ad-hoc wireless networks (2005, methodology): “There are several components to the operation of the SPAWN protocol like Peer Discovery, Peer and Content Selection, and Content Discovery and Selection.”
- Small-World Phenomena and the Dynamics of Information (2001, methodology): “In joint work with Kempe and Demers [12], we have studied how distributions that are inverse-polynomial in the distance between nodes can be used in the design of gossip protocols for spreading information in a network of communicating agents.”
- Randomized gossip algorithms (2006, unknown): “For an extensive body of related work, see [11], [16], [17], [20], [23], [25], [26], [29], [34], [42], [44], [46].”
- Design and Analysis of Algorithms (2009, unknown): “Utilizing fast algorithms for rectangular matrix multiplication has proved to be quite useful for improving the complexity of solving many of those problems (a very partial list includes [16,25,7,36,27,34,35,23,24]).”
- Kelips: Building an Efficient and Stable P2P DHT through Increased Memory and Background Overhead (2003, background): “Consider a system with n = 100, 000 nodes over k = ⌈√n⌉ = 317 affinity groups.”