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GRAPH-CONNECTIVITY

all_node_cuts

Returns all minimum k cutsets of an undirected graph G.

Example of usage

Assess collaboration network resilience.

Usage / limitations

+Use when: Assess network robustness, bottlenecks and alternative paths.

−Limitations: Graph topology and assumed relationships drive the result; missing links change connectivity.

Mathematical formula

See method documentation: implementation and assumptions vary.
Sample dataShow 
{
  "variable": "teams",
  "domain": "Workforce collaboration and organisational resilience",
  "feature_names": [
    "employee",
    "department"
  ],
  "nodes": [
    [
      "Ana",
      "Design"
    ],
    [
      "Ben",
      "Design"
    ],
    [
      "Cara",
      "Design"
    ],
    [
      "Dan",
      "Engineering"
    ],
    [
      "Eva",
      "Engineering"
    ],
    [
      "Finn",
      "Engineering"
    ]
  ],
  "edges": [
    [
      "Ana",
      "Ben"
    ],
    [
      "Ben",
      "Cara"
    ],
    [
      "Cara",
      "Ana"
    ],
    [
      "Dan",
      "Eva"
    ],
    [
      "Eva",
      "Finn"
    ],
    [
      "Finn",
      "Dan"
    ],
    [
      "Cara",
      "Dan"
    ]
  ]
}

Code example (Python)

# DataLens.ai example — collaboration network resilience.
# Copy input + code + test includes the named dataset shown above.
import networkx as nx

# Build a six-person collaboration network with two teams and one bridge.
G = nx.Graph()
G.add_nodes_from([name for name, _ in teams["nodes"]])
G.add_edges_from(teams["edges"])

# Verify the result on the example network.
cuts = list(nx.all_node_cuts(G))
assert {"Cara", "Dan"} in cuts or {"Cara"} in cuts or {"Dan"} in cuts
print("Expected output verified")