<｜begin▁of▁sentence｜>### Instruction:
In the context of a financial ledger, imagine we are tracing a series of linked transactions represented as a linear chain within our accounting system. We've metaphorically constructed this sequence as a path graph using the "nx.path_graph(10)" function, resulting in a ledger with 10 connected nodes, each representing a transaction.

As a bookkeeper trying to discern distinct clusters of transactions for better ledger organization, we're interested in employing a method analogous to the Girvan-Newman algorithm. The aim is to segregate the chain of transactions into separate groups for easier analysis and audit.

We achieve this by meticulously examining the ledger to identify the transactions (edges) that act as critical bridges between different groups (high betweenness centrality). Starting from the most significant transaction, we remove these bridges one by one. 

How do we systematically pinpoint and remove such transactions to reveal the initial grouping of transactions? Let's visualize this as peeling layers off our bookkeeping network to expose the core clusters, akin to how one might organize entries into more manageable sections within a ledger or a gml file, if we were to be analyzing digital transaction data.
### Response:
import networkx as nx
from networkx.algorithms.community import girvan_newman

# Create a graph
G = nx.path_graph(10)

# Using the Girvan-Newman algorithm to find communities
communities_generator = girvan_newman(G)

# Get the first level of communities
first_communities = next(communities_generator)

# Convert communities to sorted lists and print
first_communities = tuple(sorted(list(community)) for community in first_communities)
print(first_communities)
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