<｜begin▁of▁sentence｜>### Instruction:
Given a small weighted social network graph where the nodes represent individuals and the edges represent the strength of their connections (e.g., frequency of interactions), how can you generate a visualizable 2D layout that displays the network's structure while taking into account the edge weights and grouping nodes by communities? Additionally, ensure that overlapping nodes are separated for better visualization and reproducibility is important; therefore, a random seed is required.
### Response:
import networkx as nx
from graspologic.layouts import layout_tsne

# Create a synthetic weighted graph
G = nx.Graph()
G.add_edge('A', 'B', weight=4.7)
G.add_edge('B', 'C', weight=1.2)
G.add_edge('C', 'D', weight=3.1)
G.add_edge('D', 'A', weight=2.6)

# Generate the 2D layout of the graph
largest_cc, node_positions = layout_tsne(graph=G, perplexity=30, n_iter=1000, max_edges=10000000, weight_attribute='weight', random_seed=42, adjust_overlaps=True)

# Print each node position
for node_position in node_positions:
    print(node_position)
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