Boolean-Width Calculator

distinct neighborhood types

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About Boolean-Width Calculator

A boolean-width calculator computing boolw(G) = min over decompositions of max log₂ of distinct neighborhoods across cuts. boolw ≤ rw always. Captures 'number of types' of vertices. Many problems solvable in 2^O(boolw). Client-side.

Boolean-Width Calculator Features

  • boolw(G)
  • log₂ types
  • vs rw
  • 2^O(bw) FPT
  • Common graphs
Boolean-width boolw(G): at each cut (A, V\A), count distinct neighborhoods N(v)∩A for v in V\A. Width = log₂ of max such count. boolw ≤ rw. Many NP-hard problems solvable in time n^O(1) · 2^O(boolw). Elegant counting-based parameter.

How to Use

Select graph:

  • boolw: Boolean-width
  • Types: Distinct nbrs
  • vs rw: Compare

Distinct Neighborhoods

For a cut (A, B): two vertices in B have the same 'type' if they see the same subset of A. Boolean-width counts (log₂ of) how many distinct types exist. Fewer types → simpler structure.

Algorithmic Applications

Maximum independent set, dominating set, chromatic number: all solvable in O*(2^boolw) time. Boolean-width can be much smaller than treewidth for dense graphs. Practical for many real-world networks.

Step-by-Step Instructions

  1. 1Select graph.
  2. 2Compute boolw.
  3. 3Count types.
  4. 4Compare with rw.
  5. 5Apply algorithms.

Boolean-Width Calculator — Frequently Asked Questions

How does boolean-width compare to rank-width?+

boolw ≤ rw always. Can be much smaller! Boolean-width counts distinct neighborhoods (exponential in rw). For many practical graphs, boolean-width is small even when rank-width is moderate.

What are 'neighborhood types'?+

For a partition (A,B): vertex v in B has type = N(v)∩A (its neighbors in A). All vertices with the same type behave identically across the cut. Fewer types = more structure to exploit.

Is boolean-width practical?+

Yes! Many real-world graphs (social networks, biological networks) have small boolean-width. Algorithms using boolean-width are often more practical than treewidth-based ones for dense graphs.

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