There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as ``modularity'' over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

/pdf/finding-community-structure-in-networks-using-the-4em5so8glh.pdf

Finding community structure in networks using the eigenvectors of matrices

We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living in R d , the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such as the approximate minimum spanning tree. The article is based on the material from the authors' STOC'98 and FOCS'01 papers. It unifies, generalizes and simplifies the results from those papers.

/pdf/approximate-nearest-neighbors-towards-removing-the-curse-of-27lmlz5prr.pdf

Approximate nearest neighbors: towards removing the curse of dimensionality

Computational geometry is concerned with the design and analysis of algorithms for geometrical problems. In addition, other more practically oriented, areas of computer science— such as computer graphics, computer-aided design, robotics, pattern recognition, and operations research—give rise to problems that inherently are geometrical. This is one reason computational geometry has attracted enormous research interest in the past decade and is a well-established area today. (For standard sources, we refer to the survey article by Lee and Preparata [19841 and to the textbooks by Preparata and Shames [1985] and Edelsbrunner [1987bl.) Readers familiar with the literature of computational geometry will have noticed, especially in the last few years, an increasing interest in a geometrical construct called the Voronoi diagram. This trend can also be observed in combinatorial geometry and in a considerable number of articles in natural science journals that address the Voronoi diagram under different names specific to the respective area. Given some number of points in the plane, their Voronoi diagram divides the plane according to the nearest-neighbor

Voronoi diagrams—a survey of a fundamental geometric data structure

We describe a method for producing a greedy embedding of any n -vertex simple graph G in the hyperbolic plane, so that a message M between any pair of vertices may be routed by having each vertex that receives M pass it to a neighbor that is closer to M 's destination. Our algorithm produces succinct drawings, where vertex positions are represented using O (logn ) bits and distance comparisons may be performed efficiently using these representations.

/pdf/succinct-greedy-graph-drawing-in-the-hyperbolic-plane-1itf8guu44.pdf

Succinct Greedy Graph Drawing in the Hyperbolic Plane

We use circle-packing methods to generate quadrilateral meshes for polygonal domains, with guaranteed bounds both on the quality and the number of elements. We show that these methods can generate meshes of several types: (1) the elements form the cells of a Voronoi diagram, (2) all elements have two opposite 90° angles, (3) all elements are kites, or (4) all angles are at most 120°. In each case the total number of elements is O(n), where n is the number of input vertices.

/pdf/quadrilateral-meshing-by-circle-packing-1d9553prh3.pdf

Quadrilateral meshing by circle packing

We perform linear programming optimizations on the intersection of k polyhedra in R3, represented by their outer recursive decompositions, in expected time O(k log k log n + √k log k log3 n). We use this result to derive efficient algorithms for dynamic linear programming problems in which constraints are inserted and deleted, and queries must optimize specified objective functions. As an application, we describe an improved solution to the planar 2-center problem. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

/pdf/dynamic-three-dimensional-linear-programming-4qbjnrmuis.pdf

Dynamic Three-Dimensional Linear Programming

We consider the problem of finding a 1-planar drawing for a general graph, where a 1-planar drawing is a drawing in which each edge participates in at most one crossing. Since this problem is known to be NP-hard we investigate the parameterized complexity of the problem with respect to the vertex cover number, tree-depth, and cyclomatic number. For these parameters we construct fixed-parameter tractable algorithms. However, the problem remains NP-complete for graphs of bounded bandwidth, pathwidth, or treewidth.

Parameterized Complexity of 1-Planarity

This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages.

David Eppstein

Papers

Succinct Greedy Graph Drawing in the Hyperbolic Plane

Quadrilateral meshing by circle packing

Dynamic Three-Dimensional Linear Programming

Parameterized Complexity of 1-Planarity

UOBPRM: A uniformly distributed obstacle-based PRM