3 citations · 3 across the 8 of their papers we have counts for
7 papers · 1 filter
The Power of Graph Doubling: Computing Ultrabubbles in a Bidirected Graph by Reducing to Weak Superbubbles
Sebastian Schmidt, Juha Harviainen, Corentin Moumard +3
Bidirected graphs are a common generalisation of directed graphs where arcs can also be incoming to both their incident nodes, or outgoing from both their incident nodes. Such arcs…
Exact and Approximate Algorithms for Polytree Learning
Juha Harviainen, Frank Sommer, Manuel Sorge
Polytrees are a subclass of Bayesian networks that seek to capture the conditional dependencies between a set of variables as a directed forest and are motivated by their more…
Identifying bubble-like subgraphs in linear-time via a unified SPQR-tree framework
Francisco Sena, Aleksandr Politov, Corentin Moumard +6
A fundamental algorithmic problem in computational biology is to find all subgraphs of a given type (superbubbles, snarls, and ultrabubbles) in a directed or bidirected input graph…
Identifying all snarls and superbubbles in linear-time, via a unified SPQR-tree framework
Francisco Sena, Aleksandr Politov, Corentin Moumard +4
Snarls and superbubbles are fundamental pangenome decompositions capturing variant sites. These bubble-like structures underpin key tasks in computational pangenomics, including st…
Graph Reconstruction with a Connected Components Oracle
Juha Harviainen, Pekka Parviainen
In the Graph Reconstruction (GR) problem, the goal is to recover a hidden graph by utilizing some oracle that provides limited access to the structure of the graph. The interest is…
Quantum Speedups for Bayesian Network Structure Learning
Juha Harviainen, Kseniya Rychkova, Mikko Koivisto
The Bayesian network structure learning (BNSL) problem asks for a directed acyclic graph that maximizes a given score function. For networks with nodes, the fastest known algor…