What particularly puzzles me: Using my example graph I nowhere see the right answer in the gt output: edgelist = np.array([[ 0, 1], [ 0, 3], [ 0, 5], [ 0, 9], [ 1, 2], [ 1, 4], [ 1, 5], [ 1, 8], [ 1, 16], [ 1, 17], [ 1, 23], [ 3, 5], [ 3, 19], [ 5, 6], [ 5, 9], [ 5, 11], [ 5, 13], [ 6, 7], [ 6, 10], [ 8, 12], [ 8, 21], [ 9, 15], [ 9, 22], [11, 13], [13, 14], [13, 21], [15, 20], [15, 24], [17, 18]]) edgeweights = np.array([2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1]) G = Graph(directed=False) G.add_edge_list(edgelist) weight = G.new_edge_property('int') weight.a = edgeweights graph_draw(G, edge_pen_width=weight, vertex_text=G.vertex_index, output_size=(300, 300)) Visual inspection shows that the largest bicomponent is the set of 9 vertices {0, 1, 3, 5, 8, 9, 11, 13, 21}. bicomp, articulation, nc = label_biconnected_components(G, eprop=weight) print(bicomp.a)

[16 16 16 16 0 1 16 16 12 14 15 16 11 4 16 16 16 2 3 10 16 7 8 16 9 16 5 6 13] print(articulation.a) [0 1 0 1 0 1 1 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0] nc array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 13], dtype=uint64)

How do I recover the largest component? Also, my example is a weighted graph, but the method is agnostic of edge weights. So what can eprop be used for? Best wishes Haiko Von: graph-tool [mailto:graph-tool-bounces@skewed.de] Im Auftrag von Lietz, Haiko Gesendet: Dienstag, 5. Juni 2018 14:38 An: 'graph-tool@skewed.de' Betreff: [graph-tool] Extract largest biconnected component Hi all, There is a function to label the edges of biconnected components in a graph: https://graph-tool.skewed.de/static/doc/topology.html#graph_tool.topology.la... I'm struggling to interpret the output of the example given there. comp.a is an edge property map with integers labeling edges. But what are those values? And how can I use this property map to extract the *largest* bicomponent? Is it about binarizing the map and using it in G.set_edge_filter()? Many thanks in advance, list Haiko