dress-graph 0.4.0

DRESS: A deterministic, parameter-free framework for canonical graph fingerprinting via continuous structural edge refinement.

dress-graph (Python)

A Continuous Framework for Structural Graph Refinement

DRESS is a provably continuous relaxation of the Weisfeiler–Leman algorithm. At depth k, higher-order DRESS is provably at least as powerful as (k+2)-WL in expressiveness — the base algorithm (k=0) already matches 2-WL, and each level adds one WL dimension. Yet it is dramatically cheaper to compute: a single DRESS run costs O(I · m · d_max) where I is the number of iterations, and depth-k requires C(n,k) independent runs — a total of O(C(n,k) · I · m · d_max), compared to O(n^(k+3)) for (k+2)-WL. Space complexity is O(n + m), compared to O(n^(k+2)) for (k+2)-WL. The algorithm is embarrassingly parallel in two orthogonal ways — across the C(n,k) subproblems and across edge updates within each iteration — enabling distributed/cloud and multi-core/GPU/SIMD implementations.

Install

pip install dress-graph

Quick start

from dress import dress_fit

result = dress_fit(
    n_vertices=4,
    sources=[0, 1, 2, 0],
    targets=[1, 2, 3, 3],
)
print(result.edge_dress)  # DRESS value for each edge

For the full API and documentation, see the main repository.