python,neo4j,tinkerpop,graph-tool

I think that the workflow you present is probably the best and only one available to you. You In TinkerPop terms, I would say that the workflow would be more specifically: run query - Use the Gremlin Console find a subset of a graph - Write your traversal in the...

Yes, this is easy. You have to obtain a property map of the weighted degrees, and then do a histogram: d = g.degree_property_map("out", weight) # weight is an edge property map bins = linspace(d.a.min(), d.a.max(), 40) # linear bins h = vertex_hist(g, d, bins) ...

You should create a filtered graph first. You can do: u = GraphView(g, efilt=rel_need) where rel_need is a Boolean property map, where rel_need[e] == True means that the edge is not filtered out. You can then proceed to do the DFS search with the graph u, and the edges for...

Graph-tool now includes a function to add a list of edges to the graph. You can now do, for instance: adj = numpy.random.randint(0, 2, (100, 100)) # a random directed graph g = Graph() g.add_edge_list(transpose(adj.nonzero())) ...

This has been answered in graph-tool's mailing list: http://lists.skewed.de/pipermail/graph-tool/2015-June/002043.html In short, you should use the function g.add_edge_list(), as you said, and and put the weights separately via the array interface for property maps: e_weight.a = weight_list The weight list should have the same ordering as the edges you passed to...

You pass the fit_view = False option to graph_draw(), it will not attempt to scale the drawing to fit the output size. You can then choose your view by changing the posions of the nodes, such that it does not exclude any part of the graph: g = random_graph(10, lambda:...