<｜begin▁of▁sentence｜>### Instruction:
Given a dynamic network representing temporal interactions between nodes, captured in an existing 'dynetx' graph object 'dg', with interactions added over 10 time steps, how would you apply the TILES algorithm to identify and update communities with an observation interval of 2? Once the dynamic community detection is performed, how can you generate a JSON representation of the detected temporal communities?
### Response:
import networkx as nx
import dynetx as dn
from cdlib import algorithms

dg = dn.DynGraph()
# Assuming an existing dynetx graph dg
# Suppose interactions are added here with time steps
for x in range(10):
    g = nx.erdos_renyi_graph(200, 0.05)
    dg.add_interactions_from(list(g.edges()), t=x)

# Apply TILES algorithm for dynamic community detection with an observation interval of 2
coms = algorithms.tiles(dg, 2)

# Get a JSON representation of the TemporalClustering
coms_json = coms.to_json()
print(coms_json)
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