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On the threshold of intractability

Authors: Pål Grønås Drange; Markus Fanebust Dregi; Daniel Lokshtanov; Blair D. Sullivan;

On the threshold of intractability

Abstract

We study the computational complexity of the graph modification problems Threshold Editing and Chain Editing, adding and deleting as few edges as possible to transform the input into a threshold (or chain) graph. In this article, we show that both problems are NP-complete, resolving a conjecture by Natanzon, Shamir, and Sharan (Discrete Applied Mathematics, 113(1):109--128, 2001). On the positive side, we show the problem admits a quadratic vertex kernel. Furthermore, we give a subexponential time parameterized algorithm solving Threshold Editing in $2^{O(\surd k \log k)} + \text{poly}(n)$ time, making it one of relatively few natural problems in this complexity class on general graphs. These results are of broader interest to the field of social network analysis, where recent work of Brandes (ISAAC, 2014) posits that the minimum edit distance to a threshold graph gives a good measure of consistency for node centralities. Finally, we show that all our positive results extend to the related problem of Chain Editing, as well as the completion and deletion variants of both problems.

Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, F.2.2; G.2.2, Parameterized complexity, tractability and kernelization, Computer Science - Social and Information Networks, G.2.2, threshold graphs, Computational Complexity (cs.CC), Computer Science - Computational Complexity, Graph theory (including graph drawing) in computer science, edge editing, Computer Science - Data Structures and Algorithms, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Data Structures and Algorithms (cs.DS), F.2.2, parameterized complexity

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%
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bronze