<｜begin▁of▁sentence｜>### Instruction:
Scenario Title: Content Navigation Structure Analysis

As an Information Architect tasked with designing a clear and user-friendly digital content structure for a new educational platform, I'm delving into the underlying network relations that represent our content modules and the pathways users might take to navigate through them. For our current prototype, we have crafted an initial content map comprised of six main topics, which we've labeled for simplicity's sake as 0, 1, 2, 3, 4, and 5.

Now, in the web of interconnections (we can think of them as hyperlinks or prerequisites), we've noted the following direct relationships between topics: a link from 0 to 1 and 2, bidirectional connections from 1 to 2, 3, and 4, and a sole pathway from 4 to 5. To visualize this, one might draw these connections pictorially as edges forming a graph structure.

The concern arises as we aim for a straightforward user experience; we'd like to ascertain whether this layout is devoid of what's referred to as "asteroidal triples" (AT) — configurations that could induce confusion and disorientation during navigation, deflecting from our goal of an intuitive educational journey.

In the quest to cultivate a streamlined learning environment, it would be invaluable for our design considerations to confirm whether the described graph structure is AT-free. This analysis will drive our decisions on whether to maintain or modify the current topic connections.

Furthermore, as we explore alternative navigation patterns, we have pondered a cyclic arrangement – a "round-robin" format, if you will – where each topic leads to the next in a looped sequence, totaling six nodes. Here as well, we aim to determine the presence or absence of asteroidal triples, upholding our principle of user-friendly navigation. These assessments are foundational to ensuring the platform’s information architecture supports a logical and engaging user experience.
### Response:
import networkx as nx

# Function to check if a graph is AT-free
def is_at_free(G):
    return nx.is_at_free(G)

# Example usage:
# Create a NetworkX graph
G = nx.Graph([(0,1),(0,2),(1,2),(1,3),(1,4),(4,5)])

# Check if the graph is AT-free
result = is_at_free(G)
print(f'The graph is AT-free: {result}')

# Create another NetworkX graph
cycle_graph = nx.cycle_graph(6)

# Check if the cycle graph is AT-free
result_cycle = is_at_free(cycle_graph)
print(f'The cycle graph is AT-free: {result_cycle}')
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