<｜begin▁of▁sentence｜>### Instruction:
Imagine you are advising a client who is keen on optimizing their investment portfolio, which can be visualized as a network of financial instruments interconnected through various dependencies. The relationships in this network are represented as follows: the bonds between Investment A and Investment B, Investment B and Investment C, Investment C and Investment D, Investment D and Investment A, plus additional linkages from Investment A to Investment C, and from Investment B to Investment D.

Your client is interested in a strategic analysis that involves calculating the 'treewidth' of this investment network, which is akin to assessing the complexity within the network of investments. In financial terms, you might compare it to evaluating the diversification of a portfolio. The method your client has asked you to use is akin to the 'Minimum Degree heuristic', a technique to minimize risk by analyzing the minimal points of connection within a portfolio.

Additionally, the client is requesting a 'tree decomposition' of the network, a structure that would dismantle the network into a tree-like hierarchy, revealing the dependencies and the interconnections between these investments in a more digestible format.

This practical application will help you provide a clearer insight into the portfolio's structure and suggest potential strategies for rebalancing or simplifying the investment network. Therefore, we are not just looking at the numbers but analyzing the underlying structure of their financial landscape. How would you go about processing this request using the given data and methodology?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3), (2, 4)])

# Get treewidth decomposition
(treewidth, tree_decomp) = nx.treewidth_min_degree(G)

print(f'Treewidth: {treewidth}')
print('Tree Decomposition:')
for edge in tree_decomp.edges():
    print(edge)
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