Reconstructing semi-directed level-1 networks using few quarnets
Semi-directed networks are partially directed graphs that model evolution where the directed edges represent reticulate evolutionary events. We present an algorithm that reconstructs binary $n$-leaf semi-directed level-1 networks in $O( n^2)$ time from its quarnets (4-leaf subnetworks). Our method assumes we have direct access to all quarnets, yet uses only an asymptotically optimal number of $O(n \log n)$ quarnets. When the network is assumed to contain no triangles, our method instead relies only on four-cycle quarnets and the splits of the other quarnets. A variant of our algorithm works with quartets rather than quarnets and we show that it reconstructs most of a semi-directed level-1 network from an asymptotically optimal $O(n \log n)$ of the quartets it displays. Additionally, we provide an $O(n^3)$ time algorithm that reconstructs the tree-of-blobs of any binary $n$-leaf semi-directed network with unbounded level from $O(n^3)$ splits of its quarnets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
When are quarnets sufficient to reconstruct semi-directed phylogenetic networks?
Phylogenetic networks are graphs that are used to represent evolutionary relationships between different taxa. They generalize phylogenetic trees since for example, unlike trees, they permit lineages to combine. Recently…
Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes
Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and late…
Semi-Supervised High Dynamic Range Image Reconstructing via Bi-Level Uncertain Area Masking
Reconstructing high dynamic range (HDR) images from low dynamic range (LDR) bursts plays an essential role in the computational photography. Impressive progress has been achieved by learning-based algorithms which requir…
Directed hypergraph neural network
To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method…
General ClassificationNode ClassificationOn Learning a Hidden Directed Graph with Path Queries
In this paper, we consider the problem of reconstructing a directed graph using path queries. In this query model of learning, a graph is hidden from the learner, and the learner can access information about it with path…