We propose an exact algorithm for the graph Burning Problem (GBP), an NP-hard optimization problem that models the spread of influence on social networks. Given a graph G with vertex set V, the objective is to find a ...
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A Kirchhoff graph is a vector graph with orthogonal cycles and vertex cuts. An algorithm has been developed that constructs all the Kirchhoff graphs up to a fixed edge multiplicity. This algorithm is used to explore t...
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The problem of computing vertex and edge connectivity of a graph are classical problems in algorithmic graph theory. The focus of this paper is on computing these parameters for graphs drawn on the plane. A typical ex...
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Developing efficient graph algorithms implementations is an extremely important problem of modern computer science, since graphs are frequently used in various real-world applications. graph algorithms typically belon...
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Developing efficient graph algorithms implementations is an extremely important problem of modern computer science, since graphs are frequently used in various real-world applications. graph algorithms typically belong to the data-intensive class, and thus using architectures with high-bandwidth memory potentially allows to solve many graph problems significantly faster compared to modern multicore CPUs. Among other supercomputer architectures, vector systems, such as the SX family of NEC vector supercomputers, are equipped with high-bandwidth memory. However, the highly irregular structure of many real-world graphs makes it extremely challenging to implement graph algorithms on vector systems, since these implementations are usually bulky and complicated, and a deep understanding of vector architectures hardware features is required. This paper presents the world first attempt to develop an efficient and simultaneously simple graph processing framework for modern vector systems. Our vector graph library (VGL) framework targets NEC SX-Aurora TSUBASA as a primary vector architecture and provides relatively simple computational and data abstractions. These abstractions incorporate many vector-oriented optimization strategies into a high-level programming model, allowing quick implementation of new graph algorithms with a small amount of code and minimal knowledge about features of vector systems. In this paper, we evaluate the VGL performance on four widely used graph processing problems: breadth-first search, single source shortest paths, connected components, and page rank. The provided comparative performance analysis demonstrates that the VGL-based implementations achieve significant acceleration over the existing high-performance frameworks and libraries: up to 14 times speedup over multicore CPUs (Ligra, Galois, GAPBS) and up to 3 times speedup compared to NVIDIA GPU (Gunrock, NVgraph) implementations.
A distance sensitivity oracle is a data structure answering queries that ask the shortest distance from a node to another in a network expecting node/edge failures. It has been mainly studied in theory literature, but...
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A distance sensitivity oracle is a data structure answering queries that ask the shortest distance from a node to another in a network expecting node/edge failures. It has been mainly studied in theory literature, but all the existing oracles for a directed graph suffer from prohibitive preprocessing time and space. Motivated by this, we develop two practical distance sensitivity oracles for directed graphs as variants of Transit Node Routing. The first oracle consists of a novel fault-tolerant index structure, which is used to construct a solution path and to detect and localize the impact of network failures, and an efficient query algorithm for it. The second oracle is made by applying the A* heuristics to the first oracle, which exploits lower bound distances to effectively reduce search space. In addition, we propose additional speed-up techniques to make our oracles faster with a slight loss of accuracy. We conduct extensive experiments with real-life datasets, which demonstrate that our oracles greatly outperform all of competitors in most cases. To the best of our knowledge, our oracles are the first distance sensitivity oracles that handle real-world graph data with million-level nodes.
We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot...
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We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.
Typical crack detection processes in digital images produce a binary-segmented image that constitutes the basis for all of the following analyses. Binary images are, however, an unsatisfactory data format for advanced...
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Typical crack detection processes in digital images produce a binary-segmented image that constitutes the basis for all of the following analyses. Binary images are, however, an unsatisfactory data format for advanced crack analysis algorithms due to their sparse nature and lack of significant data structuring. Therefore, this work instead proposes a new approach based on Euclidean graphs as functional crack pattern descriptors for all post-detection analyses. Conveying both geometrical and topological information in an integrated representation, Euclidean graphs are an ideal structure for efficient crack path description, as they precisely locate the cracks on the original image and capture salient crack skeleton features. Several Euclidean graph-based algorithms for autonomous crack refining, correlation and analysis are described, with significant advantages in both their capabilities and implementation convenience over the traditional, binary image-based approach. Moreover, Euclidean graphs allow the autonomous selection of specific cracks or crack parts based on objective criteria. Well-known performance metrics, namely precision, recall, intersection over union and F1-score, have been adapted for use with Euclidean graphs. The automated generation of Euclidean graphs from binary-segmented images is also reported, enabling the application of this technique to most existing detection methods (e.g., threshold-based or neural network-based) for cracks and other curvilinear features in digital images.
The METRIC DIMENSION problem asks for a minimum-sized resolving set in a given (unweighted, undirected) graph G. Here, a set S subset of V (G) is resolving if no two distinct vertices of G have the same distance vecto...
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The METRIC DIMENSION problem asks for a minimum-sized resolving set in a given (unweighted, undirected) graph G. Here, a set S subset of V (G) is resolving if no two distinct vertices of G have the same distance vector to S. The complexity of METRIC DIMENSION in graphs of bounded treewidth remained elusive in the past years. Recently, Bonnet and Purohit [IPEC 2019] showed that the problem is W[1]-hard under treewidth parameterization. In this work, we strengthen their lower bound to show that METRIC DIMENSION is NP-hard in graphs of treewidth 24.
We investigate the structure of graphs of twin-width at most 1, and obtain the following results: graphs of twin-width at most 1 are permutation graphs. In particular they have an intersection model and a linear struc...
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graphs, comprising nodes, edges, and additional information known as attributed graphs, are essential for many applications which represents the complex data structures. Accessing these types of graphs is a significan...
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