Runtime Algorithms And Batch Compute

This example shows breadth-first search, weighted shortest path, weakly connected components, PageRank, seeded random walk, and compute_batch over the same graph.

Python

import json
from tonggraph import Graph

graph = Graph()
a = graph.add_node("a")
b = graph.add_node("b")
c = graph.add_node("c")
d = graph.add_node("d")
e = graph.add_node("e")

graph.add_edge(a, b, "LINK", properties={"weight": 2.0})
graph.add_edge(a, c, "LINK", properties={"weight": 1.0})
graph.add_edge(c, b, "LINK", properties={"weight": 0.5})
graph.add_edge(b, d, "LINK", properties={"weight": 1.0})

result = {
    "bfs_depth_1": graph.bfs(a, max_depth=1),
    "weighted_shortest_path": graph.shortest_path(a, b, weight_property="weight"),
    "connected_components": graph.connected_components(),
    "pagerank": {
        str(k): round(v, 6)
        for k, v in sorted(graph.pagerank(iterations=25, tolerance=1e-12).items())
    },
    "random_walk_seed_7": graph.random_walk(a, 4, seed=7),
    "batch": graph.compute_batch(
        [
            {"op": "bfs", "start": a, "max_depth": 1},
            {
                "op": "shortest_path",
                "start": a,
                "target": b,
                "weight_property": "weight",
            },
        ]
    ),
}

print(json.dumps(result, indent=2, sort_keys=True))

Output:

{
  "batch": [
    [
      0,
      1,
      2
    ],
    {
      "distance": 1.5,
      "nodes": [
        0,
        2,
        1
      ]
    }
  ],
  "bfs_depth_1": [
    0,
    1,
    2
  ],
  "connected_components": [
    [
      0,
      1,
      2,
      3
    ],
    [
      4
    ]
  ],
  "pagerank": {
    "0": 0.107503,
    "1": 0.283405,
    "2": 0.153192,
    "3": 0.348397,
    "4": 0.107503
  },
  "random_walk_seed_7": [
    0,
    1,
    3
  ],
  "weighted_shortest_path": {
    "distance": 1.5,
    "nodes": [
      0,
      2,
      1
    ]
  }
}