<｜begin▁of▁sentence｜>### Instruction:
In the context of preparing documentation for a case involving the structural analysis of a network, our legal team has encountered a scenario in which a tripartite agreement, metaphorically represented by a simple undirected graph, is under scrutiny. Within this graph, Party 1 maintains agreements with both Party 2 and Party 3. However, there is notably no direct agreement between Party 2 and Party 3 themselves. The legal team seeks to examine this arrangement more closely, considering the influence and relationships between the involved parties.

To aid in this investigative process, the team is considering the use of a specialized statistical tool known as Degree Adjusted Laplacian (DAD), which can be derived from the graph's adjacency matrix. They aim to employ the 'to_laplacian' function, provided by the 'graspologic' library, as a mechanism to transform the given adjacency matrix into the DAD form. This transformation would facilitate a more nuanced analysis of the inter-party relationships and their respective degrees of influence within the agreement network.

Could you rephrase the need to convert the adjacency matrix into a Degree Adjusted Laplacian matrix using the 'to_laplacian' function from the 'graspologic' library into a practical legal research scenario, while ensuring that the original semantics of the query remain intact?
### Response:
import numpy as np
from graspologic.utils import to_laplacian

# Adjacency matrix of the graph
a = np.array([
   [0, 1, 1],
   [1, 0, 0],
   [1, 0, 0]])

# Convert adjacency matrix to Laplacian with the DAD form
laplacian_matrix = to_laplacian(a, form='DAD')
print(laplacian_matrix)
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