arXiv subjects

Eunjin Oh

Publications and source records attributed to Eunjin Oh.

At least 37 records · Page 2Linked to original sources

arXiv2023

Algorithms for Computing Maximum Cliques in Hyperbolic Random Graphs

In this paper, we study the maximum clique problem on hyperbolic random graphs. A hyperbolic random graph is a mathematical model for analyzing scale-free networks since it effectively explains the power-law degree distribution of scale-free networks. We propose a simple algorithm for finding a maximum clique in hyperbolic random graph. We first analyze the running time of our algorithm theoretically. We can compute a maximum clique on a hyperbolic random graph $G$ in $O(m + n^{4.5(1-α)})$ expected time if a geometric representation is given or in $O(m + n^{6(1-α)})$ expected time if a geometric representation is not given, where $n$ and $m$ denote the numbers of vertices and edges of $G$, respectively, and $α$ denotes a parameter controlling the power-law exponent of the degree distribution of $G$. Also, we implemented and evaluated our algorithm empirically. Our algorithm outperforms the previous algorithm [BFK18] practically and theoretically. Beyond the hyperbolic random graphs, we have experiment on real-world networks. For most of instances, we get large cliques close to the optimum solutions efficiently.

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arXiv2022

Parameterized Algorithm for the Disjoint Path Problem on Planar Graphs: Exponential in $k^2$ and Linear in $n$

In this paper, we study the \textsf{Planar Disjoint Paths} problem: Given an undirected planar graph $G$ with $n$ vertices and a set $T$ of $k$ pairs $(s_i,t_i)_{i=1}^k$ of vertices, the goal is to find a set $\mathcal P$ of $k$ pairwise vertex-disjoint paths connecting $s_i$ and $t_i$ for all indices $i\in\{1,\ldots,k\}$. We present a $2^{O(k^2)}n$-time algorithm for the \textsf{Planar Disjoint Paths} problem. This improves the two previously best-known algorithms: $2^{2^{O(k)}}n$-time algorithm [Discrete Applied Mathematics 1995] and $2^{O(k^2)}n^6$-time algorithm [STOC 2020].

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arXiv2021

Feedback Vertex Set on Geometric Intersection Graphs

In this paper, we present an algorithm for computing a feedback vertex set of a unit disk graph of size $k$, if it exists, which runs in time $2^{O(\sqrt{k})}(n+m)$, where $n$ and $m$ denote the numbers of vertices and edges, respectively. This improves the $2^{O(\sqrt{k}\log k)}n^{O(1)}$-time algorithm for this problem on unit disk graphs by Fomin et al. [ICALP 2017]. Moreover, our algorithm is optimal assuming the exponential-time hypothesis. Also, our algorithm can be extended to handle geometric intersection graphs of similarly sized fat objects without increasing the running time.

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arXiv2021

Linear-Time Approximation Scheme for k-Means Clustering of Affine Subspaces

In this paper, we present a linear-time approximation scheme for $k$-means clustering of \emph{incomplete} data points in $d$-dimensional Euclidean space. An \emph{incomplete} data point with $Δ>0$ unspecified entries is represented as an axis-parallel affine subspaces of dimension $Δ$. The distance between two incomplete data points is defined as the Euclidean distance between two closest points in the axis-parallel affine subspaces corresponding to the data points. We present an algorithm for $k$-means clustering of axis-parallel affine subspaces of dimension $Δ$ that yields an $(1+ε)$-approximate solution in $O(nd)$ time. The constants hidden behind $O(\cdot)$ depend only on $Δ, ε$ and $k$. This improves the $O(n^2 d)$-time algorithm by Eiben et al.[SODA'21] by a factor of $n$.

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arXiv2021

Reachability Problems for Transmission Graphs

Let $P$ be a set of $n$ points in the plane where each point $p$ of $P$ is associated with a radius $r_p>0$.The transmission graph $G=(P,E)$ of $P$ is defined as the directed graph such that $E$ contains an edge from $p$ to $q$ if and only if $|pq|\leq r_p$ for any two points $p$ and $q$ in $P$, where $|pq|$ denotes the Euclidean distance between $p$ and $q$. In this paper, we present a data structure of size $O(n^{5/3})$ such that for any two points in $P$, we can check in $O(n^{2/3})$ time if there is a path in $G$ between the two points. This is the first data structure for answering reachability queries whose performance depends only on $n$ but not on the number of edges.

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arXiv2020

The Maximum-Level Vertex in an Arrangement of Lines

Let $L$ be a set of $n$ lines in the plane, not necessarily in general position. We present an efficient algorithm for finding all the vertices of the arrangement $A(L)$ of maximum level, where the level of a vertex $v$ is the number of lines of $L$ that pass strictly below $v$. The problem, posed in Exercise~8.13 in de Berg etal [BCKO08], appears to be much harder than it seems, as this vertex might not be on the upper envelope of the lines. We first assume that all the lines of $L$ are distinct, and distinguish between two cases, depending on whether or not the upper envelope of $L$ contains a bounded edge. In the former case, we show that the number of lines of $L$ that pass above any maximum level vertex $v_0$ is only $O(\log n)$. In the latter case, we establish a similar property that holds after we remove some of the lines that are incident to the single vertex of the upper envelope. We present algorithms that run, in both cases, in optimal $O(n\log n)$ time. We then consider the case where the lines of $L$ are not necessarily distinct. This setup is more challenging, and the best we have is an algorithm that computes all the maximum-level vertices in time $O(n^{4/3}\log^{3}n)$. Finally, we consider a related combinatorial question for degenerate arrangements, where many lines may intersect in a single point, but all the lines are distinct: We bound the complexity of the weighted $k$-level in such an arrangement, where the weight of a vertex is the number of lines that pass through the vertex. We show that the bound in this case is $O(n^{4/3})$, which matches the corresponding bound for non-degenerate arrangements, and we use this bound in the analysis of one of our algorithms.

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arXiv2019

Computing the Center Region and Its Variants

We present an $O(n^2\log^4 n)$-time algorithm for computing the center region of a set of $n$ points in the three-dimensional Euclidean space. This improves the previously best known algorithm by Agarwal, Sharir and Welzl, which takes $O(n^{2+ε})$ time for any $ε> 0$. It is known that the combinatorial complexity of the center region is $Ω(n^2)$ in the worst case, thus our algorithm is almost tight. We also consider the problem of computing a colored version of the center region in the two-dimensional Euclidean space and present an $O(n\log^4 n)$-time algorithm.

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arXiv2019

Computing a Geodesic Two-Center of Points in a Simple Polygon

Given a simple polygon $P$ and a set $Q$ of points contained in $P$, we consider the geodesic $k$-center problem where we want to find $k$ points, called \emph{centers}, in $P$ to minimize the maximum geodesic distance of any point of $Q$ to its closest center. In this paper, we focus on the case for $k=2$ and present the first exact algorithm that efficiently computes an optimal $2$-center of $Q$ with respect to the geodesic distance in $P$.

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arXiv2019

Polygon Queries for Convex Hulls of Points

We study the following range searching problem: Preprocess a set $P$ of $n$ points in the plane with respect to a set $\mathcal{O}$ of $k$ orientations % , for a constant, in the plane so that given an $\mathcal{O}$-oriented convex polygon $Q$, the convex hull of $P\cap Q$ can be computed efficiently, where an $\mathcal{O}$-oriented polygon is a polygon whose edges have orientations in $\mathcal{O}$. We present a data structure with $O(nk^3\log^2n)$ space and $O(nk^3\log^2n)$ construction time, and an $O(h+s\log^2 n)$-time query algorithm for any query $\mathcal{O}$-oriented convex $s$-gon $Q$, where $h$ is the complexity of the convex hull. Also, we can compute the perimeter or area of the convex hull of $P\cap Q$ in $O(s\log^2n)$ time using the data structure.

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arXiv2019

Faster Algorithms for Growing Prioritized Disks and Rectangles

Motivated by map labeling, Funke, Krumpe, and Storandt [IWOCA 2016] introduced the following problem: we are given a sequence of $n$ disks in the plane. Initially, all disks have radius $0$, and they grow at constant, but possibly different, speeds. Whenever two disks touch, the one with the higher index disappears. The goal is to determine the elimination order, i.e., the order in which the disks disappear. We provide the first general subquadratic algorithm for this problem. Our solution extends to other shapes (e.g., rectangles), and it works in any fixed dimension. We also describe an alternative algorithm that is based on quadtrees. Its running time is $O\big(n \big(\log n + \min \{ \log Δ, \log Φ\}\big)\big)$, where $Δ$ is the ratio of the fastest and the slowest growth rate and $Φ$ is the ratio of the largest and the smallest distance between two disk centers. This improves the running times of previous algorithms by Funke, Krumpe, and Storandt [IWOCA 2016], Bahrdt et al. [ALENEX 2017], and Funke and Storandt [EuroCG 2017]. Finally, we give an $Ω(n\log n)$ lower bound, showing that our quadtree algorithms are almost tight.

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arXiv2019

On Romeo and Juliet Problems: Minimizing Distance-to-Sight

We introduce a variant of the watchman route problem, which we call the quickest pair-visibility problem. Given two persons standing at points $s$ and $t$ in a simple polygon $P$ with no holes, we want to minimize the distance they travel in order to see each other in $P$. We solve two variants of this problem, one minimizing the longer distance the two persons travel (min-max) and one minimizing the total travel distance (min-sum), optimally in linear time. We also consider a query version of this problem for the min-max variant. We can preprocess a simple $n$-gon in linear time so that the minimum of the longer distance the two persons travel can be computed in $O(\log^2 n)$ time for any two query positions $s,t$ where the two persons start.

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arXiv2018

Point Location in Incremental Planar Subdivisions

We study the point location problem in incremental (possibly disconnected) planar subdivisions, that is, dynamic subdivisions allowing insertions of edges and vertices only. Specifically, we present an $O(n\log n)$-space data structure for this problem that supports queries in $O(\log^2 n)$ time and updates in $O(\log n\log\log n)$ amortized time. This is the first result that achieves polylogarithmic query and update times simultaneously in incremental (possibly disconnected) planar subdivisions. Its update time is significantly faster than the update time of the best known data structure for fully-dynamic (possibly disconnected) planar subdivisions.

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arXiv2018

A New Balanced Subdivision of a Simple Polygon for Time-Space Trade-off Algorithms

We are given a read-only memory for input and a write-only stream for output. For a positive integer parameter s, an s-workspace algorithm is an algorithm using only $O(s)$ words of workspace in addition to the memory for input. In this paper, we present an $O(n^2/s)$-time $s$-workspace algorithm for subdividing a simple polygon into $O(\min\{n/s,s\})$ subpolygons of complexity $O(\max\{n/s,s\})$. As applications of the subdivision, the previously best known time-space trade-offs for the following three geometric problems are improved immediately: (1) computing the shortest path between two points inside a simple $n$-gon, (2) computing the shortest path tree from a point inside a simple $n$-gon, (3) computing a triangulation of a simple $n$-gon. In addition, we improve the algorithm for the second problem even further.

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arXiv2018

Assigning Weights to Minimize the Covering Radius in the Plane

Given a set $P$ of $n$ points in the plane and a multiset $W$ of $k$ weights with $k\leq n$, we assign each weight in $W$ to a distinct point in $P$ to minimize the maximum weighted distance from the weighted center of $P$ to any point in $P$. In this paper, we give two algorithms which take $O(k^2n^2\log^3 n)$ time and $O(k^5n\log^3k+kn\log^3 n)$ time, respectively. For a constant $k$, the second algorithm takes only $O(n\log^3n)$ time, which is near linear.

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arXiv2018

Point Location in Dynamic Planar Subdivisions

We study the point location problem on dynamic planar subdivisions that allows insertions and deletions of edges. In our problem, the underlying graph of a subdivision is not necessarily connected. We present a data structure of linear size for such a dynamic planar subdivision that supports sublinear-time update and polylogarithmic-time query. Precisely, the amortized update time is $O(\sqrt{n}\log n(\log\log n)^{3/2})$ and the query time is $O(\log n(\log\log n)^2)$, where $n$ is the number of edges in the subdivision. This answers a question posed by Snoeyink in the Handbook of Computational Geometry. When only deletions of edges are allowed, the update time and query time are just $O(α(n))$ and $O(\log n)$, respectively.

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arXiv2018

Approximate Range Queries for Clustering

We study the approximate range searching for three variants of the clustering problem with a set $P$ of $n$ points in $d$-dimensional Euclidean space and axis-parallel rectangular range queries: the $k$-median, $k$-means, and $k$-center range-clustering query problems. We present data structures and query algorithms that compute $(1+\varepsilon)$-approximations to the optimal clusterings of $P\cap Q$ efficiently for a query consisting of an orthogonal range $Q$, an integer $k$, and a value $\varepsilon>0$.

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arXiv2018

The Geodesic Farthest-point Voronoi Diagram in a Simple Polygon

Given a set of point sites in a simple polygon, the geodesic farthest-point Voronoi diagram partitions the polygon into cells, at most one cell per site, such that every point in a cell has the same farthest site with respect to the geodesic metric. We present an $O(n\log\log n+m\log m)$- time algorithm to compute the geodesic farthest-point Voronoi diagram of $m$ point sites in a simple $n$-gon. This improves the previously best known algorithm by Aronov et al. [Discrete Comput. Geom. 9(3):217-255, 1993]. In the case that all point sites are on the boundary of the simple polygon, we can compute the geodesic farthest-point Voronoi diagram in $O((n + m) \log \log n)$ time.

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arXiv2018

Finding Pairwise Intersections of Rectangles in a Query Rectangle

We consider the following problem: Preprocess a set $\mathcal{S}$ of $n$ axis-parallel boxes in $\mathbb{R}^d$ so that given a query of an axis-parallel box in $\mathbb{R}^d$, the pairs of boxes of $\mathcal{S}$ whose intersection intersects the query box can be reported efficiently. For the case that $d=2$, we present a data structure of size $O(n\log n)$ supporting $O(\log n+k)$ query time, where $k$ is the size of the output. This improves the previously best known result by de Berg et al. which requires $O(\log n+ k\log n)$ query time using $O(n\log n)$ space. There has been no result known for this problem for higher dimensions, except that for $d=3$, the best known data structure supports $O(\sqrt{n}\log^2n+k\log^2n)$ query time using $O(n\sqrt {n}\log n)$ space. For a constant $d>2$, we present a data structure supporting $O(n^{1-δ}\log^{d-1} n + k \text{ polylog } n)$ query time for any constant $1/d\leqδ<1$. The size of the data structure is $O(n^{δd - 2δ+ 1}\log n)$.

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