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Adolfo Piperno is a computer scientist at Sapienza University of Rome whose work includes algorithms for graph isomorphism and graph canonical labeling. His institutional profile identifies him in the Department of Computer Science.^[ref]

Piperno introduced the approach implemented in Traces graph isomorphism software, studying how to reduce the search needed for canonical labeling. His paper examines refinement of vertex classes, the choice of vertices to individualize, search order and the use of discovered graph automorphisms.^[ref]

An original small example illustrates individualization-refinement graph search. For the four-vertex path $1-2-3-4$, grouping vertices by their numbers of neighbors first gives classes $\{1,4\}$ and $\{2,3\}$. Give vertex1 a unique marker. Neighbor counts relative to that singleton then distinguish vertex2 from vertex3, and further refinement separates the remaining vertices. Choosing vertex4 instead produces the reflected ordering. A canonical-labeling procedure must account for such equivalent choices rather than depend on the input vertex names.

This example explains the basic mechanism, not every heuristic in Traces. Piperno and Brendan McKay later described both nauty and Traces within a common framework and compared their behavior on graph families.^[ref]

nauty is software for computing graph automorphisms and canonical graph labeling, originally developed by Brendan McKay. It is distributed with Traces graph isomorphism software and auxiliary graph-generation and manipulation tools.^[ref]

An automorphism result describes relabelings that preserve adjacency. For an original example, the graph with6vertices and no edges permits every vertex permutation, so its automorphism group is the symmetric group $S_6$ of orderA compact generator description can use a transposition $(12)$ and a cycle $(123456)$. Conjugating the transposition by powers of the cycle produces adjacent transpositions, which generate the whole symmetric group. These are illustrative generators, not a claim about the exact generator list returned by a particular run.

A canonical result serves another purpose. If $\mathrm{can}(G)$ denotes the chosen canonical graph encoding, correctness requiresRenaming vertices changes the input representation but must not change that chosen result. This permits duplicate elimination when generating graphs and comparison of structures supplied with unrelated labels.

nauty combines structural refinement with a search over unresolved choices, exploiting discovered graph automorphisms to reduce equivalent work. Its theoretical framework and relationship to Traces are described in the authors' joint paper.^[ref]

A generating set for symmetries and a canonical encoding are different outputs. The first describes equivalences within a graph; the second makes equivalence between independently labeled graphs directly testable.

Traces is software for computing graph automorphisms and canonical graph labeling, developed by Adolfo Piperno and distributed with nauty. A canonical result permits two graphs to be compared without depending on their input vertex names. Traces uses individualization-refinement graph search, with strategies designed to reduce the explored search space.^[ref]

Refinement groups vertices according to structural information, including their numbers of neighbors in existing vertex classes. When this does not distinguish all vertices, individualization temporarily gives a selected vertex its own class and refinement continues. Discovering equivalent choices and distinguishing branches can avoid repeated work; the validity of the framework and implementation strategies are developed with Brendan McKay.^[ref]

An original example shows why a coarse invariant is insufficient. A cycle on6vertices and two separate triangles both have6vertices and every vertex has2neighbors. Starting with one vertex class, neighbor-count refinement leaves one class in both cases. Yet the graphs are not isomorphic: one is connected and the other is not. Their correct canonical graph encodings must therefore differ.

Conversely, two differently numbered copies of the same6-cycle should give the same canonical result when processed with the same canonicalization convention. The example illustrates the required behavior, not a benchmark of a particular Traces version.

Performance depends on the graph family and search strategy. The practical success of the program does not by itself establish a polynomial worst-case bound for graph isomorphism. A reproducible comparison should keep the canonicalization settings consistent rather than assume all programs must choose an identical representative.