<｜begin▁of▁sentence｜>### Instruction:
Certainly. Let's rephrase your scenario to match the context of a Banker's professional environment:

Imagine you're working with a hypothetical operational network representing transactions within a banking system, containing a total of 100 individual transaction points. Each pair of these points has a 10% chance of conducting a transaction with each other, resembling an Erdős-Rényi model in network theory. In an effort to streamline our operations and better understand the underlying structure of this network, you'd like to apply a sophisticated algorithm, akin to the Leiden algorithm used in network analysis, with the objective of grouping these transaction points into communities. The grouping is determined by seeking to enhance a financial efficiency metric similar to what network scientists refer to as "modularity". 

You're interested in discerning which transaction points tend to cluster together, potentially indicating operational efficiencies or redundancies within this model banking network. Could you reconfigure this scenario and describe how we might identify such clusters or "communities" of transaction points to better understand the organizational structure of our network?
### Response:
import networkx as nx
from graspologic.partition import leiden
import numpy as np

# Step 1: Construct the network graph
# For simplicity, we'll create a synthetic graph
np.random.seed(42)
G = nx.erdos_renyi_graph(100, 0.1)

# Apply the Leiden algorithm to identify communities
communities = leiden(G)

# Print the discovered community for each node
for node, community in communities.items():
    print(f'Node {node} is in community {community}')
<|EOT|>
