<｜begin▁of▁sentence｜>### Instruction:
As an urban designer tasked with optimizing community spaces, imagine you're working on revitalizing a neighborhood with 540 households. Utilizing network analysis, you want to simulate social interactions within this community. You decide to model it using an Erdos-Renyi graph, where each household represents a node, and the probability of two households being connected is 0.3. Now, you're interested in understanding smaller social circles within this network. To achieve this, you plan to sample a subgraph containing 40 households using the NonBackTrackingRandomWalkSampler method. Once you have this sample, you aim to identify cohesive groups of households by detecting k-clique communities (where k equals 4) using the percolation method. This approach will help you identify clusters of households with strong social ties, informing your design decisions for community spaces and amenities.
### Response:
```python
import networkx as nx
from littleballoffur import NonBackTrackingRandomWalkSampler

G = nx.gnp_random_graph(n=540, p=0.3)

sampler = NonBackTrackingRandomWalkSampler(40)

sampler_G = sampler.sample(G)

result = nx.community.k_clique_communities(sampler_G, 4)

print(result)
```
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