<｜begin▁of▁sentence｜>### Instruction:
Suppose a network researcher has collected snapshots of a communication network at three different time points, represented as networkx graph objects. The researcher aims to find the largest connected component across all these snapshots by first combining them into one unified graph. How can the `multigraph_lcc_union` function from graspologic be used to achieve this, and how can the indices of nodes in the original adjacency matrices that were kept in the union be retrieved?
### Response:
import networkx as nx
from graspologic.utils import multigraph_lcc_union

# Assuming we have a list of networkx graph objects
g1 = nx.fast_gnp_random_graph(10, 0.5)
g2 = nx.fast_gnp_random_graph(10, 0.5)
g3 = nx.fast_gnp_random_graph(10, 0.5)
graphs = [g1, g2, g3]

# Find the union of all multiple graphs and compute the largest connected component
lcc_graph, inds = multigraph_lcc_union(graphs, return_inds=True)

# Display the number of nodes and edges in the largest connected component
print(lcc_graph.number_of_nodes(), lcc_graph.number_of_edges())
# inds will contain the indices of the nodes from the original graphs
print(inds)
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