<｜begin▁of▁sentence｜>### Instruction:
As a computational biologist delving into the intricate world of protein-protein interaction networks, think of yourself as a sommelier of molecular complexity. Your current endeavor is akin to presenting a vast, sophisticated wine list in a manner that's both accessible and informative. You have this expansive network—where nodes represent your distinguished proteins and edges symbolize the delicate interactions they share—akin to the nuanced relationships between terroir and varietal.

Your goal is to craft a 2D representation of this network, much like a sommelier would create a simplified yet insightful wine menu from an extensive cellar collection. In this scenario, you seek to employ a t-SNE layout to transpose the high-dimensional web of protein interactions into a 2D space—comparable to translating the robust qualities of wine into a concise tasting note. This translation into two dimensions aims to reveal clusters and intricate patterns, which could very well be the sommelier's notes on potential pairings or regional characteristics, suggesting functional groupings or intricate protein complexes within your network.

To achieve this, you're considering the appropriate computational techniques and tools—perhaps akin to a sommelier choosing the right stemware and presentation—to best capture and convey the subtleties and relationships inherent in your protein interaction network.
### Response:
import networkx as nx
from graspologic.layouts import layout_tsne
from graspologic.utils import largest_connected_component

# Construct the network graph
G = nx.erdos_renyi_graph(n=100, p=0.05)

# Generate the layout using t-SNE
seed = 42  # Seed for reproducible layout
G_lcc = largest_connected_component(G)  # Use largest connected component
positions, _ = layout_tsne(G_lcc, perplexity=30, n_iter=1000, max_edges=10000000, weight_attribute='weight', random_seed=seed, adjust_overlaps=True)

# positions is a list of NodePosition with x, y coordinates and community info
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