Papers for

network layout engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Proof confirms color patterns for planar graphs with limited edges

A proof of Wang's planar D-coloring conjecture

Abstract: We prove Wang's planar D-coloring conjecture. A D-coloring of a graph is a proper edge coloring in which every subgraph isomorphic to $K_4$ minus an edge is rainbow. Wang conjectured that every planar graph of maximum degree $Δ$ has a D-coloring with 9 colors if $Δ=4$, 10 colors if $Δ=5$, and $2Δ-1$ colors if $Δ\ge 6$. Hu, Kong and Wang proved the cases $Δ\le 5$ and $Δ\ge 33$. We prove the sharp bound $2Δ-1$ for every $Δ\ge 6$, settling the remaining cases. The proof uses discharging; the case $Δ=6$ is computer-assisted.

Fri 23 OctDiscrete Mathematics
The gist
This paper solves a longstanding math puzzle about coloring the edges of certain flat graphs so that specific small groups of edges each show different colors. The authors confirmed precise rules for how many colors are needed based on the graph’s maximum number of edges meeting at a point. Their work completes cases left unsolved by earlier researchers, using a method called discharging and computer checks for one case. This advances understanding of complex coloring problems in graph theory.
Open → 2610.33322v1

Metric planarity testing problem shown to be computationally hard

Metric Planarity Testing is Hard

Abstract: A key milestone in algorithmic graph theory is Hopcroft and Tarjan's 1974 linear-time algorithm for graph planarity testing. At a workshop in 2002, Linial and Rabinovich asked for either a polynomial-time algorithm or an NP-hardness proof for metric planarity testing: given a metric space $(X,d_X)$, determine whether there exists an edge-weighted planar graph $G=(V,E,w)$ with $X\subseteq V$ that realizes the distances in $X$, meaning that $d_G(x,y)=d_X(x,y)$ for every $x,y\in X$. This question arose in the context of research on metric embeddings and their applications in computer science since the mid-1990s. Over the past few decades, polynomial-time algorithms have been developed for several special cases. In this work, we show that metric planarity testing is NP-hard, in sharp contrast to graph planarity testing. Our reduction yields a stronger result: under the Exponential Time Hypothesis (ETH), no $2^{o(n)}$-time algorithm for metric planarity testing exists, where $n$ is the number of points in the metric. To complement our ETH lower bound, we give two algorithmic results: (i) an algorithm for computing a metric planar embedding in $2^{\tilde{O}(n^2)}$ time and (ii) a $(1+ε)$-approximation algorithm in $2^{\tilde{O}(n)}$ time for any fixed $ε\in(0,1)$. The second algorithm produces a planar graph that preserves the input metric up to a factor of $(1+ε)$.

Sun 11 OctData Structures and Algorithms
The gist
The paper deals with metric planarity testing, where the goal is to decide if a set of distances between points can come from a weighted planar graph. While checking if a graph is planar is easy and fast, the authors show that finding a planar graph that exactly matches given distances is very hard—so hard that no quick algorithm is likely to exist. They also provide very slow algorithms that do work, and a way to get an approximate solution quickly. This settles a long-standing open question raised over 20 years ago.
Open → 2610.15682v1