Export API
Graft supports the export of adjacency matrices and metadata, for use in Graph libraries such as LightGraphs. The example below shows how a workflow involving LightGraphs algorithms may look like.
using Graft import LightGraphs g = Graph(10^3, 5 * 10^4); setlabel!(g, map(string, 1 : 10^3)); julia> # Set edge properties seteprop!(g, :, rand(ne(g)), :weight); julia> seteprop!(g, :, rand(1 : 10, ne(g)), :dist); julia> # Discard low weight edges g = @query(g |> filter(e.weight >= 0.5)) Graph(1000 vertices, 24939 edges, Symbol[] vertex properties, Symbol[:weight,:dist] edge properties) julia> # Export g's adjacency matrix M = export_adjacency(g) 1000×1000 sparse matrix with 24939 Int64 nonzero entries: [33 , 1] = 1 [63 , 1] = 2 [131 , 1] = 3 [161 , 1] = 4 [224 , 1] = 5 [339 , 1] = 6 [380 , 1] = 7 [491 , 1] = 8 [667 , 1] = 9 ⋮ [524 , 1000] = 24931 [615 , 1000] = 24932 [624 , 1000] = 24933 [628 , 1000] = 24934 [638 , 1000] = 24935 [846 , 1000] = 24936 [870 , 1000] = 24937 [871 , 1000] = 24938 [956 , 1000] = 24939 julia> # Export g's edge dists D = export_edge_property(g, :dist) 1000×1000 sparse matrix with 24939 Int64 nonzero entries: [33 , 1] = 4 [63 , 1] = 4 [131 , 1] = 4 [161 , 1] = 4 [224 , 1] = 4 [339 , 1] = 8 [380 , 1] = 6 [491 , 1] = 1 [667 , 1] = 8 ⋮ [615 , 1000] = 5 [624 , 1000] = 3 [628 , 1000] = 5 [638 , 1000] = 2 [846 , 1000] = 1 [870 , 1000] = 7 [871 , 1000] = 5 [956 , 1000] = 4 julia> # Construct a LightGraphs graph lg = LightGraphs.DiGraph(M) {1000, 24939} directed graph julia> # Calculate all pair shortest paths sdists = LightGraphs.floyd_warshall_shortest_paths(lg, D).dists 1000×1000 Array{Int64,2}: 5 5 4 5 6 6 6 6 6 6 6 … 4 6 4 6 5 6 4 5 7 5 3 6 6 7 7 6 6 7 7 4 6 4 6 6 7 2 6 7 6 6 6 7 6 5 6 7 6 7 5 8 5 6 7 6 7 5 5 7 6 6 7 7 8 5 7 6 6 7 5 7 5 4 7 6 6 7 6 6 5 … 6 6 3 6 5 4 7 6 6 7 5 5 4 7 7 3 7 5 7 6 2 4 6 4 6 5 6 5 7 7 7 6 6 6 6 7 7 7 5 7 6 7 6 5 4 5 4 7 7 6 6 8 6 6 6 5 6 5 7 6 7 6 8 5 7 7 8 8 6 … 7 7 7 6 8 8 9 7 9 7 7 5 7 4 7 5 4 5 4 6 6 8 6 4 7 6 5 7 6 7 6 6 5 7 5 7 6 6 7 4 5 5 6 7 9 6 7 4 6 6 7 7 6 6 7 7 5 7 7 5 7 5 6 5 7 5 6 5 6 … 5 6 5 4 4 6 7 4 7 5 5 3 5 3 6 5 7 5 6 4 6 7 6 5 6 4 6 5 5 4 5 6 7 5 6 4 5 6 5 5 5 6 5 3 4 6 5 6 3 7 6 7 6 6 5 4 5 6 ⋮ ⋮ ⋮ ⋮ ⋮ ⋱ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 8 8 5 6 7 6 6 3 6 4 7 7 6 6 7 4 5 3 8 3 5 6 4 7 7 6 7 4 3 6 5 7 5 5 6 5 7 5 4 6 5 7 4 6 7 6 6 7 7 7 5 7 6 3 5 7 7 … 7 5 6 6 5 7 5 6 5 6 5 5 6 6 7 7 6 3 5 6 5 7 4 7 7 5 5 3 6 5 7 7 6 7 4 8 7 5 5 4 7 7 6 6 7 8 8 8 5 5 7 5 8 6 7 4 7 8 6 6 6 7 5 6 5 6 4 6 5 … 6 7 7 6 3 8 5 8 6 5 7 6 7 6 6 5 5 5 6 6 7 7 5 6 7 6 5 3 5 5 8 5 5 6 6 6 7 5 6 7 6 6 6 7 6 7 3 4 8 5 6 6 4 5 7 5 4 5 9 9 7 9 7 8 7 6 9 9 7 … 9 6 9 9 8 8 6 9 7 7 0 7 8 8 8 7 7 8 6 7 8 7 8 8 8 4 6 8 7 7 9 8 8 9 8 6 5 6 6 3 5 7 6 7 6 6 7 7 7 5 4 7 7 7 6 7 4 8 7 6 6 4 6 6 4 5 7 7 6 … 7 6 5 6 3 6 5 5 6 5 8 6 julia> # Find the shortest distances for edge pairs sdists = [sdists[e.second, e.first] for e in edges(g)]; julia> # Make the shortest distances an edge proeprty seteprop!(g, :, sdists, :shortest_distance); julia> # Calculate the graph's betweenness centrality centrality = LightGraphs.betweenness_centrality(lg); julia> # Make the centrality calculate above a vertex property setvprop!(g, :, centrality, :centrality) julia> # Display the vertex table VertexDescriptor(g) │ VertexID │ Labels │ centrality │ ├──────────┼────────┼─────────────┤ │ 1 │ 1 │ 0.00100559 │ │ 2 │ 2 │ 0.00115523 │ │ 3 │ 3 │ 0.00139773 │ │ 4 │ 4 │ 0.00161647 │ │ 5 │ 5 │ 0.00184465 │ │ 6 │ 6 │ 0.00152136 │ │ 7 │ 7 │ 0.00113189 │ │ 8 │ 8 │ 0.00147083 │ ⋮ │ 990 │ 990 │ 0.00233637 │ │ 991 │ 991 │ 0.00101592 │ │ 992 │ 992 │ 0.00152453 │ │ 993 │ 993 │ 0.00124928 │ │ 994 │ 994 │ 0.000931563 │ │ 995 │ 995 │ 0.00144064 │ │ 996 │ 996 │ 0.0008423 │ │ 997 │ 997 │ 0.00114039 │ │ 998 │ 998 │ 0.00172091 │ │ 999 │ 999 │ 0.00131404 │ │ 1000 │ 1000 │ 0.00128859 │ juila> # Display the edge table for edges where a shorter distance was found EdgeDescriptor(@query(g |> filter(e.dist != e.shortest_distance))) │ Index │ Source │ Target │ weight │ dist │ shortest_distance │ ├───────┼────────┼────────┼──────────┼──────┼───────────────────┤ │ 1 │ 1 │ 339 │ 0.544382 │ 8 │ 5 │ │ 2 │ 1 │ 667 │ 0.983177 │ 8 │ 7 │ │ 3 │ 1 │ 701 │ 0.510004 │ 7 │ 6 │ │ 4 │ 1 │ 744 │ 0.678528 │ 10 │ 3 │ │ 5 │ 1 │ 772 │ 0.89435 │ 10 │ 8 │ │ 6 │ 2 │ 98 │ 0.999188 │ 9 │ 8 │ │ 7 │ 2 │ 237 │ 0.578166 │ 10 │ 7 │ │ 8 │ 2 │ 301 │ 0.560192 │ 9 │ 6 │ │ 9 │ 2 │ 476 │ 0.602794 │ 8 │ 7 │ │ 10 │ 2 │ 525 │ 0.973554 │ 9 │ 5 │ │ 11 │ 2 │ 736 │ 0.828569 │ 8 │ 5 │ │ 12 │ 2 │ 877 │ 0.620037 │ 8 │ 5 │ ⋮ │ 10367 │ 998 │ 840 │ 0.941186 │ 8 │ 4 │ │ 10368 │ 999 │ 78 │ 0.665393 │ 5 │ 3 │ │ 10369 │ 999 │ 281 │ 0.878904 │ 9 │ 6 │ │ 10370 │ 999 │ 350 │ 0.628088 │ 6 │ 5 │ │ 10371 │ 999 │ 528 │ 0.699302 │ 10 │ 6 │ │ 10372 │ 999 │ 653 │ 0.860554 │ 9 │ 5 │ │ 10373 │ 999 │ 715 │ 0.74898 │ 9 │ 7 │ │ 10374 │ 999 │ 726 │ 0.772087 │ 9 │ 5 │ │ 10375 │ 999 │ 862 │ 0.852327 │ 7 │ 5 │ │ 10376 │ 999 │ 947 │ 0.907081 │ 7 │ 5 │ │ 10377 │ 1000 │ 370 │ 0.916611 │ 9 │ 6 │ │ 10378 │ 1000 │ 375 │ 0.902912 │ 8 │ 4 │ │ 10379 │ 1000 │ 390 │ 0.523391 │ 7 │ 5 │ │ 10380 │ 1000 │ 421 │ 0.673928 │ 9 │ 5 │
Detailed Documentation:
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Graft.export_adjacency — Function.
This method provides compatibilty with LightGraphs.jl, by returning the graph's adjacency matrix.
import LightGraphs g = Graph(10^5, 10^6) # Construct a LightGraphs DiGraph from the exported adjacency matrix LightGraphs.DiGraph(export_adjacency(g))
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Graft.export_vertex_property — Function.
This method provides compatibilty with LightGraphs.jl, by returning an array containing all values for a vertex property
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Graft.export_edge_property — Function.
This method provides compatibilty with LightGraphs.jl, by returning a SparseMatrixCSC containing all values for an edge property