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arXiv · 2002.07612

Building large k-cores from sparse graphs

Fedor V. Fomin·Danil Sagunov·Kirill Simonov
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Abstract

A popular model to measure network stability is the $k$-core, that is the maximal induced subgraph in which every vertex has degree at least $k$. For example, $k$-cores are commonly used to model the unraveling phenomena in social networks. In this model, users having less than $k$ connections within the network leave it, so the remaining users form exactly the $k$-core. In this paper we study the question whether it is possible to make the network more robust by spending only a limited amount of resources on new connections. A mathematical model for the $k$-core construction problem is the following Edge $k$-Core optimization problem. We are given a graph $G$ and integers $k$, $b$ and $p$. The task is to ensure that the $k$-core of $G$ has at least $p$ vertices by adding at most $b$ edges. The previous studies on Edge $k$-Core demonstrate that the problem is computationally challenging. In particular, it is NP-hard when $k=3$, W[1]-hard being parameterized by $k+b+p$ (Chitnis and Talmon, 2018), and APX-hard (Zhou et al, 2019). Nevertheless, we show that there are efficient algorithms with provable guarantee when the $k$-core has to be constructed from a sparse graph with some additional structural properties. Our results are 1) When the input graph is a forest, Edge $k$-Core is solvable in polynomial time; 2) Edge $k$-Core is fixed-parameter tractable (FPT) being parameterized by the minimum size of a vertex cover in the input graph. On the other hand, with such parameterization, the problem does not admit a polynomial kernel subject to a widely-believed assumption from complexity theory; 3) Edge $k$-Core is FPT parameterized by $\mathrm{tw}+k$. This improves upon a result of Chitnis and Talmon by not requiring $b$ to be small. Each of our algorithms is built upon a new graph-theoretical result interesting in its own.

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Fedor V. Fomin, Danil Sagunov, Kirill Simonov. 2020-02-18. Building large k-cores from sparse graphs. https://arxiv.org/abs/2002.07612

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