<｜begin▁of▁sentence｜>### Instruction:
Alright, let me paint you a picture for this digital maze we've got on our hands. Picture our social network as a vast virtual landscape, where each person is a node, and the connections between us – who follows who – are the edges that define the paths of communication in our community. Now, your profile, darling influencer, has started this wave of trendsetting ideas that are represented by these edges: [(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6), (3, 7), (4, 8)] – the numbers being the unique IDs of your followers and their followers, and so on.

Here's the vibe: You want to keep your finger on the pulse of how your influence is spreading through this online space. But you're looking to keep things tight – we're not trying to reach the outskirts of the universe just yet. So, you're interested in discovering who's getting inspired within just two degrees of separation from your original posts, like a ripple effect in the cyber ocean.

Imagine you're sending out virtual drones (that's our BFS iterator!) from your starting point – that's node 0, aka you, honey – to track down these waves of inspiration. But because we're all about balance, you're not looking to monitor beyond the second circle of your social universe. You want these drones to fly out, check out the scene, and report back on the succession of people getting lit by your influence, but only within your immediate and next-following circles, keeping it selective and exclusive.

In the social media lingo, we could put it like this: "If I wanted to see a real-time unfolding of my brand's direct and secondary engagement levels in my virtual community tree, without going beyond the pals of my pals, how would one elegantly craft that exclusive VIP list?"

There you have it, an enchanting little query wrapped up in a social media narrative. It's not just numbers and letters, it's your influencer empire mapping out its conquests!
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add edges
G.add_edges_from([(0, 1), (0, 2), (1, 3), (1, 4), (2, 5), (2, 6), (3, 7), (4, 8)])

# Use bfs_successors starting from node 0 with a depth limit of 2
successors = dict(nx.bfs_successors(G, source=0, depth_limit=2))
print(successors)
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