Abstract
Gossiping of a single source with multiple messages (by splitting information into pieces) has been treated only for complete graphs, shown to considerably reduce the completion time, that is, the first time at which all network nodes are informed, compared with single-message gossiping. In this paper, gossiping of a single source with multiple messages is treated, for networks modeled as certain structured graphs, wherein upper bounds of the high-probability completion time are established through a novel “dependency graph” technique. The results shed useful insights into the behavior of multiple-message gossiping and can be useful for data dissemination in sensor networks, multihopping content distribution, and file downloading in peer-to-peer networks.
Highlights
Data dissemination is a fundamental issue for information diffusion in networks [1,2,3,4,5]
(1) We develop a novel “dependency graph” analysis technique for gossip spreading and leverage it to establish a high-probability upper bound of the completion time for line graphs
Similar to that in [20], we describe the protocol of pullbased gossiping with multiple messages by a random walk following a Markov chain
Summary
Data dissemination is a fundamental issue for information diffusion in networks [1,2,3,4,5]. Several important classes of network topologies have been analyzed for gossiping of a single source with a single message, including complete graphs [6], general graphs and hypercubes [15], random graphs [15, 16], random geometric graphs [17], graphs with edge expansion [18], and graphs with vertex expansion [19]. The gossiping type of a single source with multiple messages has been treated in [10], for complete graphs. (1) We develop a novel “dependency graph” analysis technique for gossip spreading and leverage it to establish a high-probability upper bound of the completion time for line graphs.
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