@INPROCEEDINGS{Zhong_05,
    AUTHOR      = {M. Zhong and K. Shen and J. Seiferas},
    TITLE       = {Non-uniform Random Membership Management in Peer-to-Peer Networks},
    BOOKTITLE   = {In Proceedings of the IEEE INFOCOM},
    YEAR        = {2005},
    EDITOR      = {},
    PAGES       = {},
    PUBLISHER   = {},
    VOLUME      = {},
    NUMBER      = {},
    SERIES      = {},
    ADDRESS     = {Miami, FL},
    MONTH       = {March},
    NOTE        = {},
    KEY         = {},
    KEYWORDS    = {},
    ISBN        = {},
    URL         = {http://www.cs.rochester.edu/~kshen/papers/infocom2005b.pdf},
    ABSTRACT    = {Existing random membership management algorithms
provide each node with a small, uniformly random subset of global
participants. However, many applications would benefit more from
non-uniform random member subsets. For instance, non-uniform
gossip algorithms can provide distancebased propagation bounds and
thus information can reach nearby nodes sooner. In another
example, Kleinberg shows that networks with random long-links
following distance-based nonuniform distributions exhibit better
routing performance than those with uniformly randomized
topologies. In this paper, we propose a scalable non-uniform
random membership management algorithm, which provides each node
with a random membership subset with application-specified
probability distributions¡ªe.g., with probability inversely
proportional to distances. Our algorithm is the first non-uniform
random membership management algorithm with proved convergence and
bounded convergence time. Moreover, our algorithm does not put
specific restrictions on the network topologies and thus have wide
applicability.}, }