graph-tool October 2016

graph-tool@skewed.de

10 participants
25 discussions

Getting edge probabilities for SBM.
by isukritgupta 12 Oct '16

12 Oct '16

Hi Tiago, I am a new user for Graph Tool. I was trying to do a fitting approach for a graph. And to measure the goodness of fit, I intend to use log of likelihood score for the generated graph and the real graph. For this, I needed to calculate the probabilities of each edge existing in the generated graph. However, when I use the 'get_edge_prob' function for BlockState, I get values which are greater than 0 for an edge existing. Which should not be possible since log(prob) <= 0. It is supposed to return 'unnormalised log probability' of the edge. Can you please clarify? Thanks, Sukrit -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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shortest_distance / concurrency / graph view
by François Kawala 11 Oct '16

11 Oct '16

Hello, I have a rather fuzzy question about the topology.shortest_distance function. I'll try to explain my problem as clearly as I can. If I fail to be clear enough, don't waste your time, just let me know. I have a pool of workers using one single graph view instance, each work calls a function f described below. The graph view has an edge filter. def f(g, origin, targets_ids, weights): d, p = topology.shortest_distance(g, source=origin, target=targets_ids, weights=weights, pred_map=True) i = score(d) path = topology.shortest_path(g, source=origin, target=targets_ids[i], pred_map=p) return path Once in a while, the path from function f is not consistant with the graph view (it goes through disabled edges). I tried to reproduce the error in a single thread environment. The exact same inputs do not fail as observed previously (ie. with multiple workers). It seems highly unlikely, but would it be possible for the predecessor map "p" to be overwritten by the actions of an another worker in the pool ? I'm asking because I ran out of ideas to explain this problem, although it sounds dumb. Bests, François.

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About reading and drawing vertex labels
by Snehal Shekatkar 08 Oct '16

08 Oct '16

Dear all, Can anybody kindly help me with this? I have a graph stored in the .gml format with each vertex labelled by some name. I want to draw these labels on top of the vertices while drawing the graph but I couldn't find any description of that in the documentation. As of now, I am only doing: G = gt.load_graph('test.gml') gt.graph_draw(G) I am attaching an example file with labels with this email. I would highly appreciate any help. Regards Snehal -- Snehal Madhukar Shekatkar Pune India

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vertex average problem
by Snehal Shekatkar 08 Oct '16

08 Oct '16

Hello all, Somehow I am getting only a half of the actual value when I try to calculate the average clustering of the graph. For example, consider the following: G = gt.Graph(directed = False) G.add_edge_list([(0, 1), (0, 2), (1, 2)]) clust = gt.local_clustering(G) C_ave = gt.vertex_average(G, clust) print(C_ave) Since the graph is simply an undirected triangle, we should get C_ave = 1. However, I get C_ave = 0.5. Am I missing something obvious? Thank you Snehal -- Snehal Madhukar Shekatkar Pune India

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Re: [graph-tool] Meaning of vertex_corr in block stochastic?
by Tiago Peixoto 03 Oct '16

03 Oct '16

Sorry for the late reply, but there was a problem with the mailing list. ttfoley wrote > I can't figure out the precise meaning of 'proportional' to, and would > like to know where/how it enters into the the actual formulae for > stochastic blocks. I have tried playing with various limits, and of course > tried scaling the 'probability' to account for graph size. For example, > there does not appear to be a limit where all edges are within blocks, > though surely this should be possible(assuming the number of edges fits > within the blocks)? Any help would be greatly appreciated. Proportional means exactly that: The probability will be what you give, up to an overall constant, which is unimportant since the total number of edges is always fixed. To force the edges to always be inside the groups, you should set the probability to zero, whenever r != s. ----- -- Tiago de Paula Peixoto <tiago(a)skewed.de> -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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graph-tool October 2016