Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis

• URL: http://arxiv.org/abs/2004.14724v3
• Date: Mon, 6 Sep 2021 11:43:39 GMT
• Title: Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis
• Authors: Niels Gr\"uttemeier and Christian Komusiewicz
• Abstract summary: We study the problem of learning the structure of an optimal Bayesian network when additional constraints are posed on the network or on its moralized graph. We show that learning an optimal network with at most $k$ edges in the moralized graph presumably has no $f(k)cdot |I|O(1)$-time algorithm.
• Score: 7.99536002595393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of learning the structure of an optimal Bayesian network when additional constraints are posed on the network or on its moralized graph. More precisely, we consider the constraint that the network or its moralized graph are close, in terms of vertex or edge deletions, to a sparse graph class $\Pi$. For example, we show that learning an optimal network whose moralized graph has vertex deletion distance at most $k$ from a graph with maximum degree 1 can be computed in polynomial time when $k$ is constant. This extends previous work that gave an algorithm with such a running time for the vertex deletion distance to edgeless graphs [Korhonen & Parviainen, NIPS 2015]. We then show that further extensions or improvements are presumably impossible. For example, we show that learning optimal networks where the network or its moralized graph have maximum degree $2$ or connected components of size at most $c$, $c\ge 3$, is NP-hard. Finally, we show that learning an optimal network with at most $k$ edges in the moralized graph presumably has no $f(k)\cdot |I|^{O(1)}$-time algorithm and that, in contrast, an optimal network with at most $k$ arcs can be computed in $2^{O(k)}\cdot |I|^{O(1)}$ time where $|I|$ is the total input size.

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