<｜begin▁of▁sentence｜>### Instruction:
As a marketing manager, you are tasked with evaluating and comparing the performance of two different community detection algorithms, Louvain and Label Propagation, using a plot of the scoring obtained with normalized mutual information after averaging results over two runs. This comparison is crucial for ensuring that the most effective algorithm is chosen to strategize community engagement and interaction on social media platforms.

For instance, you have two synthetic graphs created using the LFR benchmark to represent social network structures. The first graph (g1) has parameters (1000, 3, 1.5, 0.5, min_community=20, average_degree=5), and the second graph (g2) has parameters (1000, 3, 1.5, 0.7, min_community=20, average_degree=5).

You are interested in using the NetworkX library in Python to evaluate and compare these algorithms, and you want to visualize the results to understand which algorithm is more effective in identifying communities within the social networks. This analysis will assist in developing and implementing marketing strategies to promote products or services and increase engagement with specific communities online.
### Response:
import networkx as nx
from cdlib import algorithms, viz, evaluation

# Create synthetic graphs using the LFR benchmark
g1 = nx.algorithms.community.LFR_benchmark_graph(1000, 3, 1.5, 0.5, min_community=20, average_degree=5)
g2 = nx.algorithms.community.LFR_benchmark_graph(1000, 3, 1.5, 0.7, min_community=20, average_degree=5)

# Names for the graphs
g_names = ["g1", "g2"]

# List to store the reference partitions
g_references = []

# Store the reference communities
for g in [g1, g2]:
    communities = {frozenset(g.nodes[v]['community']) for v in g}
    g_references.append(evaluation.NodeClustering(communities, g, method_name="reference"))

# List of community detection algorithms to evaluate
algos = [algorithms.louvain, algorithms.label_propagation]

# Plot the scoring of the algorithms using normalized mutual information
viz.plot_scoring(graph_names=g_names, graphs=[g1, g2], ref_partitions=g_references, methods=algos, scoring=evaluation.normalized_mutual_information, nbRuns=2)
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