Thank you. This was very helpful. 2015-03-19 12:27 GMT+01:00 Tamas Nepusz <[email protected]>:
> > I still have some problems. For instance, the results are highly variable > Which igraph version are you using? Earlier versions of igraph used ARPACK > for > PageRank calculations and these versions had some convergence problems, > especially in the case of disconnected graphs. > > Another problem could be your usage of the personalized=... argument. The > documentation of the R interface says this: > > Optional vector giving a probability distribution to > calculate personalized PageRank. For personalized PageRank, > the probability of jumping to a node when abandoning the > random walk is not uniform, but it is given by this vector. > The vector should contains an entry for each vertex and it > will be rescaled to sum up to one. > > So, you need to provide a vector of length 328 there if your graph has 328 > vertices, and all of its elements should be zero except the single vertex > that > your random walk should jump back to. (I wonder why the R interface gives > no > error in this case when the personalization vector is too short). > > > A damping factor closer to 0 (in comparison to the default of 0.85) makes > > it more likely to stay in the neighborhood of the personalised vertex. Is > > this correct? So a lower damping factor gives a better characterisation > of > > the closely surrounding network. May I say that? > Yes, that's true, although damping values close to zero mean that the > random > walk has only a very small chance (or no chance at all) to escape from the > seed > vertex at all. For example, if your damping value is 0.25, it means that > for > 3 out of 4 trials, the random walker will jump back to the seed, so the > probability of being one step away from the seed is only 0.25; the > probability > of being two steps away is 0.25 * 0.25 (not counting the possibility that > a two-step random walk may go back to the seed nevertheless if the > neighbor of > the seed where you jumped to has a low degree). > > > Do I get NaNs for damping=0.85 because the random walk ends far away of > my > > vertex of interest? > Nope, that is probably a bug -- either because you are using an old > version of > igraph, or because the personalize=... argument is wrong. > > > If you are interested to check my igraph object, you are invited to > > download it via: > I have checked it and it seems to be the case that the problem is in the > usage > of the personalize=... argument. If you use a vector of length > vcount(netz), > then it works: > > > pers <- rep(0, vcount(netz)) > > pers[2] <- 1 > > page.rank(netz, personalize=pers) > > order(pr$vector, decreasing=T)[1:20] > [1] 2 68 53 55 64 49 61 63 47 62 42 43 48 29 32 44 45 25 26 56 > > You can even plot the graph and color the vertices according to their > personalizd PageRank score to get an idea of how the random walk > "diffuses" on > your network. > > -- > T. > > _______________________________________________ > igraph-help mailing list > [email protected] > https://lists.nongnu.org/mailman/listinfo/igraph-help >
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