<｜begin▁of▁sentence｜>### Instruction:
As a photojournalist, let's say you're embarking on a unique project that aims to visually document the interconnected web of relationships within a bustling photographers' community. There's a need to graphically represent this network, which comprises no less than 100 individual photographers, each with their own collaborative ties, totaling a staggering 4500 connections. In order to bring this complex community to life, you're considering utilizing NetworkX to create a random yet densely woven graph that mirrors the dense network of partnerships and interactions within this group.

To ensure that this representation is accurate, you'd need a method to verify the number of photographers (nodes) and the numerous collaborative connections (edges) within the created graph. Imagine that you decide to encapsulate this elaborate network in a digital format, possibly a .gml file for ease of sharing and publication alongside your visual spread. The challenge you're facing is how to generate and validate that this virtual depiction indeed reflects the specific size and scope of connections among the 100 members of this vivid photographic collective. Could you reframe this query, preserving the original intent, in a way that aligns with the day-to-day narrative of your profession?
### Response:
import networkx as nx

# Create a dense G_{n,m} random graph with 100 nodes and 4500 edges
G = nx.dense_gnm_random_graph(100, 4500, seed=42)

# Print the number of nodes and edges in the graph
print('Number of nodes:', G.number_of_nodes())
print('Number of edges:', G.number_of_edges())
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