<｜begin▁of▁sentence｜>### Instruction:
Imagine we're on the set of a documentary series that explores the complex connections and relationships within various types of networks. In the upcoming episode featuring advances in network science, we're focusing on the challenge of distinguishing between different categories of networks. We've got a collection of graph samples, each with labels identifying its category, and we're interested in showcasing a cutting-edge tool known as the SignalSubgraph estimator from the graspologic library.

In this segment, we plan to visualize and demonstrate how this estimator can pinpoint the underlying signal subgraph - essentially a subset of nodes and edges that provides key insights into what sets these categories apart. We want to capture on camera how the tool applies certain constraints to locate these crucial patterns within our graph samples.

As we set up our scene with visual aids and graphics, we also gear up to explain to our audience the concept of 'indices' and 'mask', two critical components that represent the discovered signal subgraph. To bring this to life effectively, could we possibly discuss how these components manifest visually in our graph samples? What can viewers expect to see as the indices and mask reveal the secret structures that differentiate one class of graphs from another?

As we dive into the technicalities, we'll ensure the graspologic's SignalSubgraph estimator takes center stage, detailing its application in a way that is both informative and accessible to our viewers. How will this software highlight the patterns, and what could the results mean for the future of network analysis? Let's capture this process through the lens of our cameras and make complex network science a compelling story to tell.
### Response:
import numpy as np
from graspologic.subgraph import SignalSubgraph

# Sample graph data: 2 graphs each with 4 vertices
# Graphs are different in their connectivity

# Here is an example adjacency matrix for 2 sample graphs
graphs = np.array([[[0, 1, 1, 0],
                   [1, 0, 0, 1],
                   [1, 0, 0, 1],
                   [0, 1, 1, 0]],
                  [[0, 1, 0, 0],
                   [1, 0, 1, 0],
                   [0, 1, 0, 1],
                   [0, 0, 1, 0]]])

# Labels for the two sample graphs
labels = np.array([0, 1])

# Instantiate the SignalSubgraph estimator
sigsub = SignalSubgraph()

# Fit the estimator to the graph data
# Constraints can be an int (number of edges) or a vector (number of edges and number of incident vertices)
# In this example, we set it to 3 edges
sigsub.fit(graphs, labels, constraints=3)

# Getting the indices of the signal-subgraph
sigsub_indices = sigsub.fit_transform(graphs, labels, constraints=3)
print('Signal Subgraph:', sigsub_indices)
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