<｜begin▁of▁sentence｜>### Instruction:
As a cyber forensic analyst, you have been tasked with analyzing a network science collaboration graph that represents coauthorships within the field. Following a recent investigation, you need to apply the EnDNTM overlapping community detection algorithm in cdlib using the disjoint community partitions generated by the Louvain, Label Propagation, and Walktrap algorithms. Additionally, you are required to provide a JSON representation of the resulting NodeClustering object. This will assist in uncovering potential overlapping communities within the network, which could reveal patterns of collaboration and information flow among individuals and groups.
### Response:
import networkx as nx
from cdlib import algorithms, NodeClustering

# Load a graph
G = nx.read_gml('netscience.gml')

# Generate disjoint community partitions using different algorithms
coms_louvain = algorithms.louvain(G, weight='weight')
coms_label_prop = algorithms.label_propagation(G)
coms_walktrap = algorithms.walktrap(G)

# Using ensemble approach with EnDNTM to detect overlapping communities
ensemble = [coms_louvain, coms_label_prop, coms_walktrap]
coms_endntm = algorithms.endntm(G, ensemble, epsilon=2)

# Convert the community detection result to JSON format
coms_json = coms_endntm.to_json()

print(coms_json)
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