<｜begin▁of▁sentence｜>### Instruction:
Alright, let's step into the realm of data-driven policy analysis. In this scenario, imagine you're working on a project that analyzes the social networks within a community to assess the impact of a new public health initiative. As a policy analyst, your objective is to understand how information flows through this network and potentially identify key influencers who might aid in the successful dissemination of health-related information.

The community's social interactions have been mapped out as a graph structure, and this graph is quite extensive. You've decided to focus your analysis on a representative subset of this network to simplify your investigation and make your computations more manageable. This is where the SpikyBallSampler comes into playÃ¢ÂÂa method well-suited for extracting a neighborhood-based subgraph from a large graph structure that could emulate the closer interactions you expect to drive policy outcomes.

In this context, you've got the graph data stored in the sparse6 format, specifically in a file named 'littleballoffur6.sparse6'. To proceed with your investigation, you need to sample a subgraph that accurately reflects the broader network but is scaled down to a more workable size. YouÃ¢ÂÂre aiming to strike a balance between the size of the subgraph and the sampling probability to maintain the integrity of the network's structure and ensure the validity of your subsequent analyses.

So, let's restate your task in terms of our public policy scenario: Could you employ the SpikyBallSampler technique to extract a subgraph from the provided network data ('littleballoffur6.sparse6')? You'll need to specify the number of nodes for the subgraph and adjust the sampling probability along with any other relevant parameters to ensure that the sample provides a reliable basis for evaluating the potential impacts of the public health initiative. If we were dealing with a .gml file instead, what would be the filename for this task?
### Response:
import networkx as nx
from littleballoffur import SpikyBallSampler
G = nx.read_sparse6('littleballoffur6.sparse6')
sampler = SpikyBallSampler(number_of_nodes=10, sampling_probability=0.1, initial_nodes_ratio=0.1, seed=42, max_hops=100000, mode='fireball', max_visited_nodes_backlog=100, restart_hop_size=10, distrib_coeff=1.0)
sampler_G = sampler.sample(G)
print(sampler_G.edges(), sampler_G.nodes())
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