Information Dissemination via Network Coding

We study distributed algorithms, also known as gossip algorithms, for information dissemination in an arbitrary connected network of nodes. Distributed algorithms have applications to peer-to-peer, sensor, and ad hoc networks, in which nodes operate under limited computational, communication, and energy resources. These constraints naturally give rise to “gossip” algorithms: schemes in which nodes repeatedly communicate with randomly chosen neighbors, thus distributing the computational burden across all the nodes in the network and making the computation robust against node failures. Information dissemination based on network coding was introduced by Deb and Medard. They showed the virtue of coding by analyzing a coding algorithm for a complete graph. Although their scheme generalizes to arbitrary graphs, the analysis does not. We present analysis of this algorithm for arbitrary graphs. Specifically, we find that the information dissemination time is naturally related to the spectral properties of the underlying network graph. Our results provide insight into how the graph topology affects the performance of the coding-based information dissemination algorithm