<｜begin▁of▁sentence｜>### Instruction:
As a Fraud Investigator working on mapping out financial transaction networks to identify potential fraudulent activities, you have come across a set of financial transactions that can be represented as an adjacency list to visualize the flow of funds. The adjacency list you’ve pieced together from transaction logs reads: ['1 2 5', '2 3 4', '3 5', '4', '5'].

Your task is to employ analytical tools to construct a visualization of this network. Utilizing NetworkX, a Python library adept at the creation, manipulation, and study of the complex structures of network graphs, how can you translate this adjacency list into a graphical model using NetworkX? Moreover, once the NetworkX graph is established from this list, how can you extract and review the distinct nodes and connecting edges of the graph to aid in your investigation for any sign of irregularities or patterns of interest?
### Response:
import networkx as nx

# Given an adjacency list
lines = ["1 2 5", "2 3 4", "3 5", "4", "5"]

# Parse the adjacency list
G = nx.parse_adjlist(lines, nodetype=int)

# Print nodes and edges in the graph
print('Nodes:', list(G.nodes()))
print('Edges:', list(G.edges()))
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