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This section details on the original issue you should resolve

<issue_title>flagship</issue_title>
<issue_description>see flagship example in readme.

from py3plex.core import datasets
from py3plex.dsl import Q, UQ
from py3plex.algorithms.community_detection import multilayer_louvain

1. Load a built-in multilayer biological network (~500 nodes, 4 layers)

network = datasets.fetch_multilayer("human_ppi_gene_disease_drug")

2. Run multilayer community detection

partition_vector, Q_modularity = multilayer_louvain(network, gamma=1.2, random_state=42)
network.assign_partition(partition_vector)

3. Find master regulator candidates with uncertainty quantification

master_regulators = (
Q.nodes()
.node_type("gene") # Filter by node type
.where(degree__gt=3) # Remove peripheral genes
.uq(method="perturbation", n_samples=100, ci=0.95, seed=42) # Quantify confidence
.per_layer() # Group by layer
.compute("degree_centrality", "betweenness_centrality")
.top_k(20, "betweenness_centrality__mean") # Top 20 per layer by mean
.end_grouping()
.mutate( # Create derived influence score
influence_score=lambda row: (
row.get("degree_centrality__mean", 0) * 0.4 +
row.get("betweenness_centrality__mean", 0) * 0.6
)
)
.sort(by="influence_score", descending=True)
.limit(20) # Final top 20 candidates
.execute(network)
)

df = master_regulators.to_pandas(expand_uncertainty=True)
print(df[["id", "layer", "betweenness_centrality", "betweenness_centrality_std",
"betweenness_centrality_ci95_low", "betweenness_centrality_ci95_high",
"influence_score"]].head(10))

which other operator can we include in this example to make it even more comprehensive? (and is supported by dsl)</issue_description>

Comments on the Issue (you are @copilot in this section)