graph-tool March 2018

graph-tool@skewed.de

6 participants
14 discussions

How to control the orientation of the graph topology
by Xun Xiao 26 Feb '22

26 Feb '22

Hi Tiago, sometimes, when delete the graph and create a new one, then plot it, the plotted graph is shown upside down (in terms of the node text). So how could we control the orientation of the graph? Or which parameter we should use to always make node text shown in a correct orientation. Thanks a lot. -- 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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Upside down labels
by charlie 26 Feb '22

26 Feb '22

Hi, I'm suffering from the same issue mentioned in this post: https://git.skewed.de/count0/graph-tool/issues/174 Namely, I'm trying to draw a graph that includes a lot of self-looping edges, and my labels are being printed upside down. If I remove the self-loops the labels are shown the right way up. Is there a fix for it? Thanks, Charlie -- 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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What formula is used for std in vertex_average and edge_average?
by VaSa 16 Jul '20

16 Jul '20

I am curious what is being used to calculate the standard deviation of the average in gt.vertex_average and gt.edge_average >>> t2=gt.Graph() >>> t2.add_vertex(2) >>> t2.add_edge(t2.vertex(0), t2.vertex(1)) >>> gt.vertex_average(t2, "in") (0.5, 0.35355339059327373) Now, shouldn't std be σ(n)=sqrt(((0-0.5)^2+(1-0.5)^2)/2)=0.5 ? also q(n-1)=sqrt((0.5^2+0.5^2)/(2-1))~=0.70710 0.3535 is sqrt(2)/4 which happens to be σ(n-1)/2, so it seems there is some relation to that. A little bigger graph. >>> t3=gt.Graph() >>> t3.add_vertex(5) >>> t3.add_edge(t3.vertex(0), t3.vertex(1)) >>> gt.vertex_average(t3, "in") (0.2, 0.17888543819998318) Now, we should have 0,1,0,0,0 series for vertex incoming degree. So Windows calc gives σ(n)=0.4 and σ(n-1)~=0.44721, so where does 0.1788854 come from ? Reason, I am asking because, I have a large graph, where the average looks quite alright but the std makes no sense, as going by the histogram, degree values are quite a bit more distributed than the std would indicate. -- 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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assigning vertex properties when adding edges
by m jadidi 04 Mar '20

04 Mar '20

Hi, I was wondering if there is any way to assign vertex properties while adding edges to the graph. for example using "add_edge_list" I can assign edge properties but later I have to iterate through all vertices again to assign their properties. I know this is not a problem when the vertex property is of the type "int" or "float" because then one can use "vprop.a = values", but in case of "string" and "object" this method doesn't work What would be the best/fastest way to handle this situation. I guess it would be very helpful to extend the "add_edge_list" function to accept vertex property in some way. cheers, -- Mohsen

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Nested Block State hierarchy level after mcmc_equilibrate
by ilyco 16 Jan '20

16 Jan '20

I ran mcmc_equilibrate on a nested block state model in a weighted graph. As per instructions, I copied the initially computed state in another object with increased hierarchy depth to 10. However, this fixed the depth to 10. Everything computed afterwards has depth 10 even if is clear that after 3 or 4 levels the nodes converge to one. There are many empty branches and when I try to plot it with empty_branches = False, I get an error stating it is not a tree. RuntimeError: Invalid hierarchical tree: No path from source to target. Did anybody perform any similar analyses? The hierarchy after mcmc_equilibrate: <NestedBlockState object, with base <BlockState object with 24 blocks (24 nonempty), degree-corrected, with 1 edge covariate, for graph <Graph object, undirected, with 230 vertices and 11230 edges, edges filtered by (<PropertyMap object with key type 'Edge' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64911d0>, False), vertices filtered by (<PropertyMap object with key type 'Vertex' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64912d0>, False) at 0x7fc3a89f1210>, at 0x7fc3a6491950>, and 10 levels of sizes [(230, 24), (24, 5), (5, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)] at 0x7fc3a6491590> -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Problem with add_edge_list(eprop=x)
by JHickey 04 Oct '18

04 Oct '18

I'd like to use the "add_edge_list" function to add edges to my graph, and simultaneously assign a value for an edge property map for each of the added edges. When I try the following example (with no edge property map information) works fine: elist1 = np.array([[0,1], [1, 2]]) #edge list with no value for edge property map data (only the source and target for the edges) g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist) But with the following example, I get the error below: elist2 = np.array([[0,1, 1], [1, 2, 0]]) #edge list with edge property map value in 3rd column g = gt.Graph() my_eprop = g.new_edge_property('bool') g.add_edge_list(elist2, eprops=my_eprop) Error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-48-6a949ea52c9b> in <module>() ----> 1 g.add_edge_list(elist2, eprops=my_eprop) /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in add_edge_list(self, edge_list, hashed, string_vals, eprops) 1969 eprops = () 1970 else: -> 1971 convert = [_converter(x.value_type()) for x in eprops] 1972 eprops = [_prop("e", self, x) for x in eprops] 1973 if not isinstance(edge_list, numpy.ndarray): /usr/lib/python2.7/dist-packages/graph_tool/__init__.pyc in __getitem__(self, k) 534 kt = "Graph" 535 raise ValueError("invalid key '%s' of type '%s', wanted type: %s" --> 536 % (str(k), str(type(k)), kt) ) 537 538 def __setitem__(self, k, v): ValueError: invalid key '0' of type '<type 'int'>', wanted type: Edge --------------------------------------------------------------------------- Please help! -- 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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FW: Graphtool: Finding the paths that the maximum flow algorithm produces
by Hobé Alex 25 Jun '18

25 Jun '18

Hi Tiago and everyone else on the list Thank you for your swift reply. The attached png is one example of a max flow calculation result. The flow is limited in four places in this graph. I would like to extract the order of the edges in the four paths I drew. The shortest path solution you described corresponds to path 3 in the drawing. all_shortest_paths gives my path 3 and another completely different path before crashing with a memoryError. Looking forward to hearing from you. Best, Alex ________________________________________ From: Tiago de Paula Peixoto [tiago(a)skewed.de] Sent: Thursday, March 09, 2017 18:43 To: Hobé Alex Subject: Re: Graphtool: Finding the paths that the maximum flow algorithm produces Hi Hobé, I'm not sure I understand what you want. Any path containing edges with positive flow is a "flow path". There are many of them. Which one do you want? If you just want _some_ path, just filter out the edges with zero flow and get the shortest path. But there are many other ways to proceed... Best, Tiago PS. There is a mailing list of the graph-tool project, where questions like this can be posted. It is better to use the list than to ask me directly, since it builds a repository of questions others can consult, and other people can help you as well. On 09.03.2017 17:04, Hobé Alex wrote: > Dear Mr. de Paula Peixoto > > I am using the python graphtool for my master thesis and am enjoying the > beautiful pictures it produces. > When computing the maximum flow I would like to use other edge properties > along the computed path for further calculations. > I am therefore looking for a way to find an array, which contains the edges > along a flow path, similar to the shortest_path result. > I looked through all the pages of the online tutorial, learnt a couple of > new tricks, but haven't found a way to solve this. > > I would be very thankful for any assistance you would be able to provide and > I look forward to hearing from you. > Best wishes, > > Alex Hobé -- Tiago de Paula Peixoto <tiago(a)skewed.de>

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from graph_tool.all import * gives ImportError
by vineesh vs 21 Jun '18

21 Jun '18

I tried the following command in Python and ipython prompt (2.7 version) --------------------------------------------------------------------------- In [1]: from graph_tool.all import * --------------------------------------------------------------------------- Its giving the following error. How can we solve it? --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-1-100bbe2bc9d9> in <module>() ----> 1 from graph_tool.all import * /usr/lib/python2.7/dist-packages/graph_tool/__init__.py in <module>() 103 104 from .dl_import import * --> 105 dl_import("from . import libgraph_tool_core as libcore") 106 __version__ = libcore.mod_info().version 107 /usr/lib/python2.7/dist-packages/graph_tool/dl_import.pyc in dl_import(import_expr) 55 56 try: ---> 57 exec(import_expr, local_dict, global_dict) 58 finally: 59 sys.setdlopenflags(orig_dlopen_flags) # reset it to normal case to <string> in <module>() ImportError: /usr/lib/python2.7/dist-packages/graph_tool/libgraph_tool_core.so: undefined symbol: _ZN5boost9iostreams4zlib6finishE --------------------------------------------------------------------------- Thanks

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AttributeError: 'graph_tool::Layers<graph_tool::BlockState<boost::u' object has no attribute 'couple_state'
by Zahra Sheikhbahaee 21 Apr '18

21 Apr '18

I am trying to make a layered nested network by following command lines similar to the example in the instruction (except using nested stochastic blockmodel): LayerState = minimize_nested_blockmodel_dl(g, layers=True, state_args=dict(ec=g.edge_properties["Timestamp"], layers=True), verbose=True) bs = LayerState.get_bs() bs += [np.zeros(1)] * (L - len(bs)) LayerState = LayerState.copy(bs=bs, sampling=True) dS, nmoves = LayerState.mcmc_sweep(niter=500) I got this error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib/python3/dist-packages/graph_tool/inference/nested_blockmodel.py", line 577, in mcmc_sweep return self._h_sweep(lambda s, **a: s.mcmc_sweep(**a), c=c, **kwargs) File "/usr/lib/python3/dist-packages/graph_tool/inference/nested_blockmodel.py", line 500, in _h_sweep get_entropy_args(eargs)) File "/usr/lib/python3/dist-packages/graph_tool/inference/blockmodel.py", line 698, in _couple_state self._state.couple_state(state._state, entropy_args) AttributeError: 'graph_tool::Layers<graph_tool::BlockState<boost::u' object has no attribute 'couple_state' Any comment why it occurred or suggestion about how could it be fixed ? Thanks.

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get_edges_prob() changes the entropy of the state object
by Katharina Baum 26 Mar '18

26 Mar '18

I currently started using graph-tool: nice software, excellent documentation, thank you! However, I stumbled on some (for me) unexpected behavior when using the get_edges_prob() function with a BlockState of a weighted network (graph-tool version 2.26, Python 2.7 as well as Python 3.6). When calling the get_edges_prob() function to a state, its entropy is altered, and subsequent calls of get_edges_prob() deliver different results. "Luckily", I could reproduce the observed behavior with a dataset from the graph-tool collection (with arguably small alterations, but the introduced differences are a lot bigger in my networks). import graph_tool as gt import graph_tool.collection as gtc import graph_tool.inference as gti g=gtc.data["celegansneural"] state=gti.minimize_blockmodel_dl(g,state_args=dict(recs=[g.ep.value],rec_types=['real-normal'])) original_entropy=state.entropy() edge_prob=[] for i in range(10000): edge_prob.append(state.get_edges_prob(missing=[],spurious=[(0,2)])) original_entropy state.entropy() #entropy is different from original! edge_prob[0] #first call of get_edges_prob() delivers other results than last edge_prob[-1] For me, this is really unexpected. What is happening there, and/or how this can be fixed? Smaller further experiments showed that this also happens to NestedBlockStates (of course), but seems not to happen for models lacking edge covariates... Thanks in advance! Katharina Katharina Baum, PhD Postdoctoral Fellow, Proteome and Genome Research Unit Luxembourg Institute of Health Department of Oncology 1A-B, rue Thomas Edison, L-1445 Strassen Luxembourg Fax: +352 26970-719 Email: Katharina.Baum(a)lih.lu<mailto:Katharina.Baum@lih.lu> Website: https://www.lih.lu/ [http://files.lih.lu/outlookSignature/lih.jpg] Join us on Facebook and follow our activities on Twitter: https://www.facebook.com/Luxembourg.Institute.of.Health http://twitter.com/LIH_Luxembourg Katharina.Baum(a)lih.lu<mailto:Katharina.Baum@lih.lu> This message (including any attachments) is intended for the addressee only and may contain confidential and/or privileged information and/or information protected by intellectual property rights. If you have received this message by mistake, please notify the sender by return e-mail and delete this message from your system. You should not use, alter, copy or distribute this message or disclose its contents to anyone. E-mail transmission can not be guaranteed to be secure or error free as information could be intercepted, corrupted, lost, destroyed, arrive late or incomplete, or contain viruses. Luxembourg Institute of Health shall not be responsible nor liable for the proper and complete transmission of the information contained in this communication nor for any delay in its receipt or damage to your system.

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graph-tool March 2018