The problem of computing the exact stretch factor (i.e., the tight bound on the worst case stretch factor) of a Delaunay triangulation is one of the longstanding open problems in computational geometry. Over the years, a series of upper and lower bounds on the exact stretch factor have been obtained but the gap between them is still large. An alternative approach to solving the problem is to develop techniques for computing the exact stretch factor of "easier" types of Delaunay triangulations, in particular those defined using regular-polygons instead of a circle. Tight bounds exist for Delaunay triangulations defined using an equilateral triangle and a square. In this paper, we determine the exact stretch factor of Delaunay triangulations defined using a regular hexagon: It is 2. We think that the main contribution of this paper are the two techniques we have developed to compute tight upper bounds for the stretch factor of Hexagon-Delaunay triangulations.

Triangle counts of massive graphs can provide important information regarding the structure of the networks these graphs model. Exact triangle counting can be expensive and thus researchers have proposed a number of approximation approaches. The state-of-the-art triangle count approximation techniques depend on wedge (two-path) sampling. In this paper we offer a mechanism to significantly improve wedge sampling for triangle counting. We shrink the sampling space by eliminating wedges that are less likely to participate in triangles. Experiments over large-scale real-world graphs show that proposed mechanism provides five- to a few hundred-folds sampling space reduction. When compared against the state-of-the-art approaches, it requires as low as ~ 100 × less sampling to provide the same accuracy, or makes as low as ~ 8 × less error when used with the same sampling ratio.

The number of triangles in a graph is useful to deduce a plethora of important features of the network that the graph is modeling. However, finding the exact value of this number is computationally expensive. Hence, a number of approximation algorithms based on random sampling of edges, or wedges (adjacent edge pairs) have been proposed for estimating this value. We argue that for large sparse graphs with power-law degree distribution, random edge sampling requires sampling large number of edges before providing enough information for accurate estimation, and existing wedge sampling methods lead to biased samplings, which in turn lead to less accurate estimations. In this paper, we propose a hybrid algorithm between edge and wedge sampling that addresses the deficiencies of both approaches. We start with uniform edge sampling and then extend each selected edge to form a wedge that is more informative for estimating the overall triangle count. The core estimate we make is the number of triangles each sampled edge in the first phase participates in. This approach provides accurate approximations with very small sampling ratios, outperforming the state-of-the-art up to 8 times in sample size while providing estimations with 95% confidence.

Let ${\cal P}$ be a set of $n$ points embedded in the plane, and let ${\cal C}$ be the complete Euclidean graph whose point-set is ${\cal P}$. Each edge in ${\cal C}$ between two points $p, q$ is realized as the line segment $[pq]$, and is assigned a weight equal to the Euclidean distance $|pq|$. In this paper, we show how to construct in $O(n\lg{n})$ time a plane spanner of ${\cal C}$ of maximum degree at most 4 and stretch factor at most 20. This improves a long sequence of results on the construction of plane spanners of ${\cal C}$. Our result matches the smallest known upper bound of 4 by Bonichon et al. on the maximum degree of plane spanners of ${\cal C}$, while significantly improving their stretch factor upper bound from 156.82 to 20. The construction of our spanner is based on Delaunay triangulations defined with respect to the equilateral-triangle distance, and uses a different approach than that used by Bonichon et al. Our approach leads to a simple and intuitive construction of a well-structured spanner, and reveals useful structural properties of the Delaunay triangulations defined with respect to the equilateral-triangle distance. The structure of the constructed spanner implies that when ${\cal P}$ is in convex position, the maximum degree of this spanner is at most 3. Combining the above degree upper bound with the fact that 3 is a lower bound on the maximum degree of any plane spanner of ${\cal C}$ when the point-set ${\cal P}$ is in convex position, the results in this paper give a tight bound of 3 on the maximum degree of plane spanners of ${\cal C}$ for point-sets in convex position.

We provide a kinetic data structure (KDS) to the planar kinetic mesh refinement problem, which concerns computation of meshes of continuously moving points. Our KDS computes the Delaunay triangulation of a size-optimal well-spaced superset of a set of moving points with algebraic trajectories of constant degree. Our KDS is compact, requiring linear space in the size of the output. It is local, using a point in O(log Delta) certificates. It is responsive, repairing itself in O(log Delta) time per event. It is efficient, processing O(n2 log3 Delta) events in the worst case; this is optimal up to a polylogarithmic factor. Also, our KDS is dynamic, responding to point insertions and deletions in O(log Delta) time. In our bounds Delta stands for the geometric spread, the ratio of the diameter to the closest pair distance. To the best of our knowledge, this is the first KDS for mesh refinement.

Parallel algorithms and dynamic algorithms possess an interesting duality property: compared to sequential algorithms, parallel algorithms improve run-time while preserving work, while dynamic algorithms improve work but typically offer no parallelism. Although they are often considered separately, parallel and dynamic algorithms employ similar design techniques. They both identify parts of the computation that are independent of each other. This suggests that dynamic algorithms could be parallelized to improve work efficiency while preserving fast parallel run-time.

In a well-spaced point set, when there is a bounding hypercube, the Voronoi cells all have bounded aspect ratio, i.e., the distance from the Voronoi site to the farthest point in the Voronoi cell divided by the distance to the nearest neighbor in the set is bounded by a small constant. Well-spaced point sets satisfy some important geometric properties and yield quality Voronoi or simplicial meshes that can be important in scientific computations. In this paper, we consider the dynamic well-spaced point sets problem, which requires computing the well-spaced superset of a dynamically changing input set, e.g., as input points are inserted or deleted. We present a dynamic algorithm that allows inserting/deleting points into/from the input in worst-case O(log Δ) time, where Δ is the geometric spread, a natural measure that is bounded by O(log n) when input points are represented by log-size words. We show that the runtime of the dynamic update algorithm is optimal in the worst case. Our algorithm generates size-optimal outputs: the resulting output sets are never more than a constant factor larger than the minimum size necessary. A preliminary implementation indicates that the algorithm is indeed fast in practice. To the best of our knowledge, this is the first time- and size-optimal dynamic algorithm for well-spaced point sets.

Self-adjusting computation provides an evaluation model where computations can respond automatically to modifications to their data by using a mechanism for propagating modifications through the computation. Current approaches to self-adjusting computation guarantee correctness by recording dependencies in a trace at the granularity of individual memory operations. Tracing at the granularity of memory operations, however, has some limitations: it can be asymptotically inefficient (\eg, compared to optimal solutions) because it cannot take advantage of problem-specific structure, it requires keeping a large computation trace (often proportional to the runtime of the program on the current input), and it introduces moderately large constant factors in practice.