feat: analyze the core graph structure

graph/combine.py

@@ -132,7 +132,7 @@ def export_new_graph(g: ig.Graph, split_data: list[list[set[ig.Vertex]]]) -> ig.
         for union_index, nodes in enumerate(unions):
             index_map[(layer_index, union_index)] = g_index
             ng.vs[g_index]['step'] = layer_index
-            # ng.vs[g_index]['codes'] = [x['code'] for x in nodes]
+            ng.vs[g_index]['codes'] = [x['code'] for x in nodes]
             g_index += 1
 
     for layer_index in range(len(split_data)-1):
@@ -172,5 +172,5 @@ if __name__ == '__main__':
     # print(gg)
     # ig.plot(gg, 'demo.png', vertex_size=10)
 
-    # gg.write_pickle('main_combined.pkl')
+    gg.write_pickle('main_combined.pkl')
     # gg.write_graphml('main_combined.graphml')

graph/cut.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+
+import igraph as ig
+
+
+def split_max_graph(g: ig.Graph, cut_point: int):
+    gg = g.copy()
+    edges = [(cut_point, x.index) for x in gg.vs[cut_point].neighbors()]
+    gg.delete_edges(edges)
+    components = sorted(gg.components(), key=lambda x: -len(x))
+    return g.subgraph(components[0] + [cut_point])
+
+
+def split_subgraph(g: ig.Graph):
+
+    # def demo():
+    #     sizes = set()
+    #     for cut_point in g.articulation_points():
+    #         gg: ig.Graph = g.copy()
+    #         gg.delete_vertices(cut_point)
+    #         subs = gg.decompose()
+    #         sizes.update([x.vcount() for x in subs])
+    #     print(sorted(sizes))
+
+    # demo()
+
+    for _ in range(1034):
+        print(g.vcount(), g.ecount())
+
+        points = g.articulation_points()
+        assert len(points) > 0
+        g = split_max_graph(g, points[0])
+
+    # ig.plot(g, 'demo.png', bbox=(2000, 2000))
+    print(g.vcount(), g.ecount())
+    # g.write_graphml('main_cir.graphml')
+    g.write_pickle('main_cir.pkl')
+
+
+if __name__ == '__main__':
+    raw = ig.Graph.Read_Pickle('main_combined.pkl')
+    # print(raw.vcount())
+    split_subgraph(raw)