Approximate Association via Dissociation
October 28, 2015 Β· Declared Dead Β· π Discrete Applied Mathematics
"No code URL or promise found in abstract"
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Authors
Jie You, Jianxin Wang, Yixin Cao
arXiv ID
1510.08276
Category
cs.DS: Data Structures & Algorithms
Citations
24
Venue
Discrete Applied Mathematics
Last Checked
3 months ago
Abstract
A vertex set $X$ of a graph $G$ is an association set if each component of $G - X$ is a clique, or a dissociation set if each component of $G - X$ is a single vertex or a single edge. Interestingly, $G - X$ is then precisely a graph containing no induced $P_3$'s or containing no $P_3$'s, respectively. We observe some special structures and show that if none of them exists, then the minimum association set problem can be reduced to the minimum (weighted) dissociation set problem. This yields the first nontrivial approximation algorithm for association set, and its approximation ratio is 2.5, matching the best result of the closely related cluster editing problem. The reduction is based on a combinatorial study of modular decomposition of graphs free of these special structures. Further, a novel algorithmic use of modular decomposition enables us to implement this approach in $O(m n + n^2)$ time.
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