vive wrote: > Note that this is a natural part > of running the swapping algorithms and having some tight-connected > neighborhoods.
I see - so we're talking about clusters that already exist in the topology being reproduced in the locations; would you also expect location clustering to occur in opennet, where there are presumably no tight clusters in the topology? > Once in a while the randomized position from a newbie node will fit well > enough > with the later group, so some nodes in that cluster will want to consume (with > swapping) the newbie addresses if it lets them specialize even further (brings > them closer in keyspace, due to the nature of the swapping algorithm). That would make sense if there were only a single cluster of long-lived nodes. But haven't we always assumed that the social network contains many clusters, with a Kleinberg-like overall structure? If that's the case, there are not only strong ties pulling each cluster together - there are also weak ties between clusters, stretching the clusters out. The nodes in each cluster shouldn't become more and more tightly clustered, even if they have a constant supply of suitable locations, because the inter-cluster ties should counterbalance the intra-cluster ties. If that's not happening, it seems like the problem is with the topology (too few inter-cluster links) rather than with churn. > Using the current locations is not the same as generating the Kleinberg model > from the beginning. Really? The connection probabilities are independent, so I would have thought that if you removed a node and replaced it with a new node, the new node's links would be independent of the existing links (and thus could be generated using just the current locations). Cheers, Michael
