<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the career trajectories of two up-and-coming Hollywood stars, each navigating through their own complex series of roles and acting opportunities, similar to paths one might trace on a bustling city map. Consider that Star A's recent sequence of project commitments can be likened to traveling a route consisting of these milestones: starting with Indie Film (1), moving on to Off-Broadway Theater (2), jumping to a Summer Blockbuster (4), taking a detour through an Award-Winning Drama (3), returning to the Indie Film (1), and finally circling back to the Summer Blockbuster (4). Concurrently, Star B has their own distinct journey: beginning with a Pilot Episode (5), proceeding to a Guest Appearance (6), advancing to a Feature Film (7), stepping up to a Lead TV Role (8), looping back to the Pilot Episode (5), revisiting the Feature Film (7), making another appearance in the Lead TV Role (8), and reconnecting with the Guest Appearance (6).

In the world of movie-making, it's critical that each star maintains a resilient network of connections that enables them to thrive amidst the unpredictable waves of Hollywood, ensuring they are intertwined with at least three solid project commitments at any given point in their career.

To paint a clear picture of how robust their current networks are, I'd like you to create a visualization using the connections map (akin to a network diagram) of their roles. Could you illustrate which clusters within their respective circuits are unflappable and can weather any storm, that is to say, those groups of milestones that remain connected even if any two connections fall through?

Please employ similar visual diagrams often used in scenario planning and career forecasting for our stars. If you need to maintain a record of this analysis, you could store the information comparable to a graph file, perhaps naming it "Hollywood_Career_Paths.gml". This will help us to advise them on potential vulnerabilities in their portfolio of roles and to advocate for their career sustainability more effectively.
### Response:
import networkx as nx
from networkx.utils import pairwise
import itertools as it

G = nx.Graph()

paths = [(1, 2, 4, 3, 1, 4), (5, 6, 7, 8, 5, 7, 8, 6)]
G.add_nodes_from(it.chain(*paths))
G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))

components = list(nx.k_edge_components(G, k=3))

print('k-edge-connected components:', sorted(map(sorted, components)))
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