untangle-rs 0.2.2

Library that minimises edge crossings in a hierarchical multipartite graph

untangle-rs

Library that minimises edge crossings in weighted multipartite graphs, with support for respecting node hierarchy. It uses a simple metropolis-hastings algorithm that iteratitely swaps neighboring nodes in order to minimize the number of crossings. It is unlikely to find a global minimum but does converge to optimal solutions.

Basic example

Basic layouts

You can minimize the crossings in arbitrary multipartite graphs, as long as you provide the parts of the graph:

from untanglers import LayoutOptimizerInt

nodes = [
  [0,1,2],
  [3,4,5],
  [6,7,8],
]

edges = [
  # node_a, node_b, edge_weight
  [(0, 4, 1), (1, 3, 5)],
  [(4, 8, 2), (5, 6, 1)]
]

optimizer = LayoutOptimizerInt(nodes, edges)
edge_crossings = optimizer.count_crossings()
new_crossings = optimizer.optimize(
  start_temp = 1.0,
  end_temp = 0.1,
  steps = 3,
  max_iterations = 20,
  passes = 5,
)

More complicated graph

Hierarchical layouts

In case certain nodes need to remain grouped together, the optimizer also supports hierarchy. This is useful for visualizing e.g. data lineage where columns in a table should remain together.

from untanglers import HierarchyOptimizerInt

nodes = ...
edges = ...

hierarchy = [
  # 2 levels, note that the coarser level is aligned with the finer level
  [[4,5,6,5], [9, 11]],
  [[7, 20, 13], [27, 13]],
  [[8, 9, 6, 7], [17, 13]],
]

optimizer = HierarchyOptimizerInt(nodes, edges, hierarchy)
new_crossings = optimizer.optimize(...)

Graph with 2 levels of node hierarchy