<｜begin▁of▁sentence｜>### Instruction:
As an investment banker providing financial advisory and capital markets services, you may need to analyze the flow of information and decision-making within a network of stakeholders. Utilizing a directed graph representing relationships and interactions, you might be interested in calculating the in-degree, or convergence degree, for each node to understand the level of influence and input received by different entities within the network. Considering a hypothetical scenario, let's assume you're working with data representing connections and dependencies between various departments within a corporation. By using a library such as NetworkX in Python, you can determine the in-degree for each department node, thereby gaining insights into the extent of incoming connections and relationships driving decision-making processes.
### Response:
import networkx as nx

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

# Add edges to the graph
G.add_edges_from([(1, 2), (2, 3), (3, 4), (2, 4), (4, 5)])

# Calculate the in-degree for all nodes (convergence)
convergence_degrees = G.in_degree()

# Output the in-degree for each node
print(convergence_degrees)
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