<｜begin▁of▁sentence｜>### Instruction:
Imagine you're delving into the study of an evolving social network within a small community that mimics the intricate connections of a growing tribal system. In this system, each new tribe member forms bonds preferentially with those who are already well-connected within this social framework. These connections are not random; rather, they are influenced by the existing network of relationships, which can be thought of as a network where the tribal elders or influential members are more likely to attract new links.

To model this, you wish to create a social network map consisting of 10 individuals. As a new individual joins the tribal council, they tend to create bonds with those more prominent members, where the likelihood of forming a connection is proportional to the influence of each member, mathematically represented by their number of current connections raised to the power of 1.5 (a kind of social gravitational pull).

In anthropological terms, this scenario could be represented using a tribal council network graph, starting from the founding elder and adding one member at a time. Each new member's bond with existing members is determined by the influence of those members, with the degree of influence computed using a kernel function that scales with the degree of current connections taken to the 1.5 power.

You'd like this social model to be reflected as an undirected graph, akin to a network of interpersonal relationships where influences flow in both directions, void of hierarchical directionality. The resulting map would be invaluable in visualizing and understanding the social dynamics and connectivity of this evolving tribal council.
### Response:
import networkx as nx

# Function to represent the attachment kernel
kernel = lambda x: x ** 1.5

# Generate a GN graph with 10 nodes
D = nx.gn_graph(10, kernel=kernel)

# Convert the directed GN graph to undirected
G = D.to_undirected()

print('Nodes:', G.nodes())
print('Edges:', G.edges())
<|EOT|>
