<p>The <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>-<span>Orientation</span> problem asks whether it is possible to orient an undirected graph to a directed phylogenetic network of a desired network class <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>. This problem arises, for example, when visualising evolutionary data, as popular methods such as Neighbor-Net are distance-based and inevitably produce undirected graphs. The complexity of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>-<span>Orientation</span> remains open for many classes <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>, including binary tree-child networks, and practical methods are still lacking. In this paper, we propose (1) an exact FPT algorithm for <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>-<span>Orientation</span>, applicable to any class <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation> admitting a tractable membership test, and parameterised by the reticulation number and the maximum size of minimal basic cycles, and (2) a very fast heuristic for <span>Tree-Child Orientation</span>. While the state-of-the-art for <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\mathcal {C}\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="script">C</mi> </math></EquationSource> </InlineEquation>-<span>Orientation</span> is a simple exponential time algorithm whose computational bottleneck lies in searching for appropriate reticulation vertex placements, our methods significantly reduce this search space. Experiments show that, although our FPT algorithm is still exponential, it significantly outperforms the existing method. The heuristic runs even faster but with increasing false negatives as the reticulation number grows. Given this trade-off, we also discuss theoretical directions for improvement and biological applicability of the heuristic approach.</p>

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Orientability of undirected phylogenetic networks to a desired class: practical algorithms and application to tree-child orientation

  • Tsuyoshi Urata,
  • Manato Yokoyama,
  • Haruki Miyaji,
  • Momoko Hayamizu

摘要

The \(\mathcal {C}\) C -Orientation problem asks whether it is possible to orient an undirected graph to a directed phylogenetic network of a desired network class \(\mathcal {C}\) C . This problem arises, for example, when visualising evolutionary data, as popular methods such as Neighbor-Net are distance-based and inevitably produce undirected graphs. The complexity of \(\mathcal {C}\) C -Orientation remains open for many classes \(\mathcal {C}\) C , including binary tree-child networks, and practical methods are still lacking. In this paper, we propose (1) an exact FPT algorithm for \(\mathcal {C}\) C -Orientation, applicable to any class \(\mathcal {C}\) C admitting a tractable membership test, and parameterised by the reticulation number and the maximum size of minimal basic cycles, and (2) a very fast heuristic for Tree-Child Orientation. While the state-of-the-art for \(\mathcal {C}\) C -Orientation is a simple exponential time algorithm whose computational bottleneck lies in searching for appropriate reticulation vertex placements, our methods significantly reduce this search space. Experiments show that, although our FPT algorithm is still exponential, it significantly outperforms the existing method. The heuristic runs even faster but with increasing false negatives as the reticulation number grows. Given this trade-off, we also discuss theoretical directions for improvement and biological applicability of the heuristic approach.