<｜begin▁of▁sentence｜>### Instruction:
As an accountant, I manage financial records and prepare tax returns for individuals or organizations. Now, let's imagine a scenario where a company needs assistance with analyzing their network data to optimize resource allocation. Suppose this company, let's call it NetOpt Inc., has a network represented by an Erdos-Renyi graph created using NetworkX. The graph has 500 nodes, with each pair of nodes connected with a probability of 0.35.

In order to improve resource allocation, NetOpt Inc. wants to sample a subgraph consisting of 100 nodes from their network. However, they want the sampling process to be biased towards nodes with fewer common neighbors, as these nodes may represent underutilized resources or potential areas for optimization.

To achieve this, we can utilize the CommonNeighborAwareRandomWalkSampler provided by NetworkX. This sampler allows us to perform a random walk on the graph while considering the number of common neighbors between nodes, thus biasing the sampling towards nodes with fewer common neighbors. By integrating this sampling strategy into our analysis, we can assist NetOpt Inc. in identifying and leveraging opportunities for resource optimization within their network.
### Response:
import networkx as nx
from littleballoffur import CommonNeighborAwareRandomWalkSampler

G = nx.erdos_renyi_graph(n=500, p=0.35)

sampler = CommonNeighborAwareRandomWalkSampler(100)

sampler_G = sampler.sample(G)

print(sampler_G.nodes())
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