Undirected Edges Research Articles

Computing Quasi-Upward Planar Drawings of Mixed Graphs

A mixed graph has both directed and undirected edges. We study how to compute a crossing-free drawing of an embedded planar mixed graph, such that it is upward ‘as much as possible’. Roughly speaking, in an upward drawing of a mixed graph all (undirected) edges are monotone in the vertical direction and directed edges flow monotonically from bottom to top according to their orientation. We study quasi-upward drawings of mixed graphs, that is, upward drawings where edges can break the vertical monotonicity in a finite number of edge points, called bends. We describe both efficient heuristic techniques and exact approaches for computing quasi-upward planar drawings of embedded mixed graphs with few bends, and we extensively compare them experimentally: the results suggest that our algorithms are effective in many cases.

Latent Space Models for Dynamic Networks

Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. We propose Markov chain Monte Carlo (MCMC) algorithm to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the evolution and the structure, both local and global, of the network. The model handles directed or undirected edges, easily handles missing edges, and lends itself well to predicting future edges. Further, a novel approach is given to detect and visualize an attracting influence between actors using only the edge information. We use the case-control likelihood approximation to speed up the estimation algorithm, modifying it slightly to account for missing data. We apply the latent space model to data collected from a Dutch classroom, and a cosponsorship network collected on members of the U.S. House of Representatives, illustrating the usefulness of the model by making insights into the networks. Supplementary materials for this article are available online.

Branch-and-cut-and-price algorithms for the Degree Constrained Minimum Spanning Tree Problem

Assume that a connected undirected edge weighted graph G is given. The Degree Constrained Minimum Spanning Tree Problem (DCMSTP) asks for a minimum cost spanning tree of G where vertex degrees do not exceed given pre-defined upper bounds. In this paper, three exact solution algorithms are investigated for the problem. All of them are Branch-and-cut based and rely on the strongest formulation currently available for the problem. Additionally, to speed up the computation of dual bounds, they all use column generation, in one way or another. To test the algorithms, new hard to solve DCMSTP instances are proposed here. These instances, combined with additional ones taken from the literature, are then used in computational experiments. The experiments compare the new algorithms among themselves and also against the best algorithms currently available in the literature. As an outcome of them, one of the new algorithms stands out on top.

Connection matrices and Lie algebra weight systems for multiloop chord diagrams

We give necessary and sufficient conditions for a weight system on multiloop chord diagrams to be obtainable from a metrized Lie algebra representation, in terms of a bound on the ranks of associated connection matrices. Here a multiloop chord diagram is a graph with directed and undirected edges so that at each vertex, precisely one directed edge is entering and precisely one directed edge is leaving, and each vertex is incident with precisely one undirected edge. Weight systems on multiloop chord diagrams yield the Vassiliev invariants for knots and links. The k-th connection matrix of a function f on the collection of multiloop chord diagrams is the matrix with rows and columns indexed by k-labeled chord tangles and with entries equal to the f value on the join of the tangles.

A New Random Graph Model with Self-Optimizing Nodes: Connectivity and Diameter

We introduce a new random graph model. In our model, n, n ≥ 2, vertices choose a subset of potential edges by considering the (estimated) benefits or utilities of the edges. More precisely, each vertex selects k, k ≥ 1, incident edges it wishes to set up, and an undirected edge between two vertices is present in the graph if and only if both of the end vertices choose the edge. First, we examine the scaling law of the smallest k needed for graph connectivity with increasing n and prove that it is Θ(log (n)). Second, we study the diameter of the random graph and demonstrate that, under certain conditions on k, the diameter is close to log (n)/log (log (n)) with high probability. In addition, as a byproduct of our findings, we show that, for all sufficiently large n, if k > β⋆log (n), where β⋆ ≈ 2.4626, there exists a connected Erds–Rnyi random graph that is embedded in our random graph, with high probability.

Robust and computationally feasible community detection in the presence of arbitrary outlier nodes

Community detection, which aims to cluster $N$ nodes in a given graph into $r$ distinct groups based on the observed undirected edges, is an important problem in network data analysis. In this paper, the popular stochastic block model (SBM) is extended to the generalized stochastic block model (GSBM) that allows for adversarial outlier nodes, which are connected with the other nodes in the graph in an arbitrary way. Under this model, we introduce a procedure using convex optimization followed by $k$-means algorithm with $k=r$. Both theoretical and numerical properties of the method are analyzed. A theoretical guarantee is given for the procedure to accurately detect the communities with small misclassification rate under the setting where the number of clusters can grow with $N$. This theoretical result admits to the best-known result in the literature of computationally feasible community detection in SBM without outliers. Numerical results show that our method is both computationally fast and robust to different kinds of outliers, while some popular computationally fast community detection algorithms, such as spectral clustering applied to adjacency matrices or graph Laplacians, may fail to retrieve the major clusters due to a small portion of outliers. We apply a slight modification of our method to a political blogs data set, showing that our method is competent in practice and comparable to existing computationally feasible methods in the literature. To the best of the authors’ knowledge, our result is the first in the literature in terms of clustering communities with fast growing numbers under the GSBM where a portion of arbitrary outlier nodes exist.

Min–max cover of a graph with a small number of parts

We consider a variety of vehicle routing problems. The input consists of an undirected graph and edge lengths. Customers located at the nodes have to be visited by a set of vehicles. Two important parameters are k the number of vehicles, and the longest distance traveled by a vehicle denoted by λ. Here, we consider k to be a given bound on the maximum number of vehicles, and thus the decision maker cannot increase its value. Therefore, the goal will be to minimize λ. We study different variations of this problem, where for instance instead of servicing the customers using paths, we can serve them using spanning trees or cycles. For all these variations, we present new approximation algorithms with FPT time (where k is the parameter) which improve the known approximation guarantees for these problems.

Improving robustness of next-hop routing

A weakness of next-hop routing is that following a link or router failure there may be no routes between some source-destination pairs, or packets may get stuck in a routing loop as the protocol operates to establish new routes. In this article, we address these weaknesses by describing mechanisms to choose alternate next hops. Our first contribution is to model the scenario as the following tree augmentation problem. Consider a mixed graph where some edges are directed and some undirected. The directed edges form a spanning tree pointing towards the common destination node. Each directed edge represents the unique next hop in the routing protocol. Our goal is to direct the undirected edges so that the resulting graph remains acyclic and the number of nodes with outdegree two or more is maximized. These nodes represent those with alternative next hops in their routing paths. We show that tree augmentation is NP-hard in general and present a simple $$\frac{1}{2}$$ -approximation algorithm. We also study 3 special cases. We give exact polynomial-time algorithms for when the input spanning tree consists of exactly 2 directed paths or when the input graph has bounded treewidth. For planar graphs, we present a polynomial-time approximation scheme when the input tree is a breadth-first search tree. To the best of our knowledge, tree augmentation has not been previously studied.

General scaling of maximum degree of synchronization in noisy complex networks

The effects of white noise and global coupling strength on the maximum degree of synchronization in complex networks are explored. We perform numerical simulations of generic oscillator models with both linear and non-linear coupling functions on a broad spectrum of network topologies. The oscillator models include the Fitzhugh–Nagumo model, the Izhikevich model and the Kuramoto phase oscillator model. The network topologies range from regular, random and highly modular networks to scale-free and small-world networks, with both directed and undirected edges. We then study the dependency of the maximum degree of synchronization on the global coupling strength and the noise intensity. We find a general scaling of the synchronizability, and quantify its validity by fitting a regression model to the numerical data.

Solving the undirected feedback vertex set problem by local search

An undirected graph consists of a set of vertices and a set of undirected edges between vertices. Such a graph may contain an abundant number of cycles, in which case a feedback vertex set (FVS) is a set of vertices intersecting with each of these cycles. Constructing a FVS of cardinality approaching the global minimum value is an optimization problem in the nondeterministic polynomial-complete complexity class, and therefore it might be extremely difficult for some large graph instances. In this paper we develop a simulated annealing local search algorithm for the undirected FVS problem by adapting the heuristic procedure of Galinier et al. [P. Galinier, E. Lemamou, M.W. Bouzidi, J. Heuristics 19, 797 (2013)], which worked for the directed FVS problem. By defining an order for the vertices outside the FVS, we replace the global cycle constraints by a set of local vertex constraints on this order. Under these local constraints the cardinality of the focal FVS is then gradually reduced by the simulated annealing dynamical process. We test this heuristic algorithm on large instances of Erdös-Rényi random graph and regular random graph, and find that this algorithm is comparable in performance to the belief propagation-guided decimation algorithm.

Node-weighted measures for complex networks with directed and weighted edges for studying continental moisture recycling

In many real-world networks nodes represent agents or objects of different sizes or importance. However, the size of the nodes is rarely taken into account in network analysis, possibly inducing bias in network measures and confusion in their interpretation. Recently, a new axiomatic scheme of node-weighted network measures has been suggested for networks with undirected and unweighted edges. However, many real-world systems are best represented by complex networks which have directed and/or weighted edges. Here, we extend this approach and suggest new versions of the degree and the clustering coefficient associated to network motifs for networks with directed and/or weighted edges and weighted nodes. We apply these measures to a spatially embedded network model and a real-world moisture recycling network. We show that these measures improve the representation of the underlying systems' structure and are of general use for studying any type of complex network.

Improved Approximation for Orienting Mixed Graphs

An instance of the maximum mixed graph orientation problem consists of a mixed graph and a collection of source-target vertex pairs. The objective is to orient the undirected edges of the graph so as to maximize the number of pairs that admit a directed source-target path. This problem has recently arisen in the study of biological networks, and it also has applications in communication networks. In this paper, we identify an interesting local-to-global orientation property. This property enables us to modify the best known algorithms for maximum mixed graph orientation and some of its special structured instances, due to Elberfeld et al. (Theor. Comput. Sci. 483:96---103, 2013), and obtain improved approximation ratios. We further proceed by developing an algorithm that achieves an even better approximation guarantee for the general setting of the problem. Finally, we study several well-motivated variants of this orientation problem.

A new method to infer causal phenotype networks using QTL and phenotypic information.

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.

A Fast Algorithm to Find All High-Degree Vertices in Graphs with a Power-Law Degree Sequence

AbstractWe develop a fast method for finding all high-degree vertices of a connected graph with a power-law degree sequence. The method uses a biased random walk, where the bias is a function of the power law c of the degree sequence.Let G(t) be a t-vertex graph, with degree sequence power law c ≥ 3 generated by a generalized preferential attachment process that adds m edges at each step. Let Sa be the set of all vertices of degree at least ta in G(t). We analyze a biased random walk that makes transitions along undirected edges {x, y} with probabilities proportional to (d(x)d(y))b, where d(x) is the degree of vertex x and b > 0 is a constant parameter. With parameter b = (c − 1)(c − 2)/(2c − 3), the random walk discovers the set Sa completely in steps with high probability. The error parameter e depends on c, a, and m.The cover time of the entire graph G(t) by the biased walk is . Thus the expected time to discover all vertices by the biased walk is not much higher than the Θ(tlog t) cover time of a simp...

Tracing retinal vessel trees by transductive inference

BackgroundStructural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks.ResultsIn this paper, we consider a novel graph-based approach to address this tracing with crossover problem: After initial steps of segmentation and skeleton extraction, its graph representation can be established, where each segment in the skeleton map becomes a node, and a direct contact between two adjacent segments is translated to an undirected edge of the two corresponding nodes. The segments in the skeleton map touching the optical disk area are considered as root nodes. This determines the number of trees to-be-found in the vessel network, which is always equal to the number of root nodes. Based on this undirected graph representation, the tracing problem is further connected to the well-studied transductive inference in machine learning, where the goal becomes that of properly propagating the tree labels from those known root nodes to the rest of the graph, such that the graph is partitioned into disjoint sub-graphs, or equivalently, each of the trees is traced and separated from the rest of the vessel network. This connection enables us to address the tracing problem by exploiting established development in transductive inference. Empirical experiments on public available fundus image datasets demonstrate the applicability of our approach.ConclusionsWe provide a novel and systematic approach to trace retinal vessel trees with the present of crossovers by solving a transductive learning problem on induced undirected graphs.

Upward and quasi-upward planarity testing of embedded mixed graphs

Mixed graphs have both directed and undirected edges and have received considerable attention in the literature. We study two upward planarity testing problems for embedded mixed graphs, give some NP-hardness results, and describe Integer Linear Programming techniques to solve them. Experiments show the efficiency of our approach.

On the Upward Planarity of Mixed Plane Graphs

A mixed plane graph is a plane graph whose edge set is partitioned into a set of directed edges and a set of undirected edges. An orientation of a mixed plane graph G is an assignment of directions to the undirected edges of G resulting in a directed plane graph $\vec G$ . In this paper, we study the computational complexity of testing whether a given mixed plane graph G is upward planar, i.e., whether it admits an orientation resulting in a directed plane graph G such that G admits a planar drawing in which each edge is represented by a curve monotonically increasing in the y-direction according to its orientation. Our contribution is threefold. First, we show that the upward planarity testing problem is solvable in cubic time for mixed outerplane graphs. Second, we show that the problem of testing the upward planarity of mixed plane graphs reduces in quadratic time to the problem of testing the upward planarity of mixed plane triangulations. Third, we exhibit linear-time testing algorithms for two classes of mixed plane triangulations, namely mixed plane 3-trees and mixed plane triangulations in which the undirected edges induce a forest.

On Weak Chromatic Polynomials of Mixed Graphs

A mixed graph is a graph with directed edges, called arcs, and undirected edges. A k-coloring of the vertices is proper if colors from {1, 2, . . . , k} are assigned to each vertex such that u and v have different colors if uv is an edge, and the color of u is less than or equal to (resp. strictly less than) the color of v if uv is an arc. The weak (resp. strong) chromatic polynomial of a mixed graph counts the number of proper k-colorings. Using order polynomials of partially ordered sets, we establish a reciprocity theorem for weak chromatic polynomials giving interpretations of evaluations at negative integers.

The Trees Skeleton Extraction Based on Point Cloud Contraction

To achieve the trees three dimensional simulation, the most critical step is to extract the trees skeleton. This paper focuses on the point cloud contraction-based skeletal extraction algorithm, uses neighbors to build a transformation matrix, extract a discrete point set approximated to the real skeleton by Laplacian contraction, and constructs a 1D curve skeleton with the help of a weighted undirected graph and edge collapse algorithm. 1D curve form is more easy to operate, guide the reconstruction of three-dimensional model, solve the problem of incomplete data in the process of modeling .

An Eight-Approximation Algorithm for Computing Rooted Three-Vertex Connected Minimum Steiner Networks

For a given undirected (edge) weighted graph G = (V, E), a terminal set S ⊆ V and a root r ∈ S, the rooted k-vertex connected minimum Steiner network (kVSMNr) problem requires to construct a minimum-cost subgraph of G such that each terminal in S \ {R} is k-vertex connected to τ. As an important problem in survivable network design, the kVSMN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> problem is known to be NP-hard even when k 1/4 1 [14]. For k 1/4 3 this paper presents a simple combinatorial eight-approximation algorithm, improving the known best ratio 14 of Nutov [20]. Our algorithm constructs an approximate 3VSMN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> through augmenting a two-vertex connected counterpart with additional edges of bounded cost to the optimal. We prove that the total cost of the added edges is at most six times of the optimal by showing that the edges in a 3VSMN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">τ</sub> compose a subgraph containing our solution in such a way that each edge appears in the subgraph at most six times.