<｜begin▁of▁sentence｜>### Instruction:
Imagine a painter who is working on a series of canvases, each depicting the connections and interplay of light and shadow among the same group of trees in a forest. Every painting, while unique, captures various angles and moments, showing different interactions of the boughs intertwining with one another. The artist seeks to create a new piece that encapsulates the most cohesive and resonant tangle of branches – the very essence that is present in each individual canvas.

To achieve this, the painter must isolate the portion of the trees' network where the interplay of branches is not just incidental but a central theme across all the paintings. In other words, the painter is searching for the largest continuous cluster of intertwining branches that is a motif recurring in all their works.

How might the painter discern this fundamental motif, this common, interconnected web of branches, which is the heart of each painting's network?
### Response:
import networkx as nx
import numpy as np
from graspologic.utils import multigraph_lcc_intersection

# Step 1: Construct multiple network graphs (for example three random graphs)
G1 = nx.erdos_renyi_graph(n=100, p=0.1)
G2 = nx.erdos_renyi_graph(n=100, p=0.2)
G3 = nx.erdos_renyi_graph(n=100, p=0.3)

# Step 2: Find the largest connected component intersection
intersection_graph = multigraph_lcc_intersection(graphs=[G1, G2, G3])

# Step 3: Check if we want the indices of the original graphs
intersection_graph, inds = multigraph_lcc_intersection(graphs=[G1, G2, G3], return_inds=True)

print('Intersection Graph:', intersection_graph)
print('Indices of the original graphs kept:', inds)
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