We establish a parametric framework for obtaining obstruction characterizations of graph parameters with respect to a quasi-ordering ≤ on graphs. At the center of this framework lies the concept of a ≤-parametric gr...
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We study the task of gathering k energy-constrained mobile agents in an undirected edge-weighted graph. Each agent is initially placed on an arbitrary node and has a limited amount of energy, which constrains the dist...
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We study the task of gathering k energy-constrained mobile agents in an undirected edge-weighted graph. Each agent is initially placed on an arbitrary node and has a limited amount of energy, which constrains the distance it can move. Since this may render gathering at a single point impossible, we study three variants of near-gathering: The goal is to move the agents into a configuration that minimizes either (i) the radius of a ball containing all agents, (ii) the maximum distance between any two agents, or (iii) the average distance between the agents. We prove that (i) is polynomial-time solvable, (ii) has a polynomial-time 2-approximation with a matching NP-hardness lower bound, while (iii) admits a polynomial-time 2(1 - 1/k)-approximation, but no FPTAS, unless P = NP. We extend some of our results to additive approximation. (C) 2020 Elsevier B.V. All rights reserved.
PageRank is a commonly used measurement in a wide range of applications, including search engines, recommendation systems, and social networks. However, this measurement suffers from huge computational overhead, which...
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PageRank is a commonly used measurement in a wide range of applications, including search engines, recommendation systems, and social networks. However, this measurement suffers from huge computational overhead, which cannot be scaled to large graphs. Although many approximate algorithms have been proposed for computing PageRank values, these algorithms are either (i) not efficient or (ii) not accurate. Worse still, some of them cannot provide estimated PageRank values for all the vertices. In this paper, we first propose the CUR-Trans algorithm, which can reduce the time complexity for computing PageRank values and has lower error bound than existing matrix approximation-based PageRank algorithms. Then, we develop the T 2-Approx algorithm to further reduce the time complexity for computing this measurement. Experiment results on three large-scale graphs show that both the CUR-Trans algorithm and the T 2-Approx algorithm achieve the lowest response time for computing PageRank values with the best accuracy (for the CUR-Trans algorithm) or the competitive accuracy (for the T 2-Approx algorithm). Besides, the two proposed algorithms are able to provide estimated PageRank values for all the vertices.
We initiate the systematic algorithmic study for gerrymandering over graphs that was recently introduced by Cohen-Zemach, Lewenberg and Rosenschein. Namely, we study a strategic procedure for a political districting d...
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We initiate the systematic algorithmic study for gerrymandering over graphs that was recently introduced by Cohen-Zemach, Lewenberg and Rosenschein. Namely, we study a strategic procedure for a political districting designer to draw electoral district boundaries so that a particular target candidate can win in an election. We focus on the existence of such a strategy under the plurality voting rule, and give interesting contrasts which classify easy and hard instances with respect to polynomial-time solvability. For example, we prove that the problem for trees is strongly NP-complete (thus unlikely to have a pseudo-polynomial-time algorithm), but has a pseudo-polynomial-time algorithm when the number of candidates is constant. Another example is to prove that the problem for complete graphs is NP-complete when the number of electoral districts is two, while is solvable in polynomial time when it is more than two. (C) 2021 Elsevier B.V. All rights reserved.
Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high d...
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Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses are currently lacking. In this article, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.
In this paper, we study the problem of fast constructions of source-wise round-trip spanners in weighted directed graphs. For a source vertex set S subset of V in a graph G(V, E), an S-sourcewise round-trip spanner of...
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In this paper, we study the problem of fast constructions of source-wise round-trip spanners in weighted directed graphs. For a source vertex set S subset of V in a graph G(V, E), an S-sourcewise round-trip spanner of G of stretch k is a subgraph H of G such that for every pair of vertices u, v is an element of S x V, their round-trip distance in H is at most k times of their round-trip distance in G. We show that for a graph G(V, E) with n vertices and m edges, an s-sized source vertex set S subset of V and an integer k >1, there exists an algorithm that in time O (ms(1/k) log(5) n) constructs an S-sourcewise round-trip spanner of stretch O(k log n) and O (ns(1/k) log(2) n) edges with high probability. Compared to the fast algorithms for constructing all-pairs round-trip spanners [26,12], our algorithm improves the running time and the number of edges in the spanner when k is super-constant. Compared with the existing algorithm for constructing source-wise round-trip spanners [36], our algorithm significantly improves their construction time c2(min{ms, n omega}) (where omega is an element of [2, 2.373) and 2.373 is the matrix multiplication exponent) to nearly linear O (ms1/k log5 n), at the expense of paying an extra O (log n) in the stretch. As an important building block of the algorithm, we develop a graph partitioning algorithm to partition G into clusters of bounded radius and prove that for every u, v is an element of S x V at small round-trip distance, the probability of separating them in different clusters is small. The algorithm takes the size of S as input and does not need the knowledge of S. With the algorithm and a reachability vertex size estimation algorithm, we show that the recursive algorithm for constructing standard round-trip spanners [26] can be adapted to the source-wise setting. We rigorously prove the correctness and computational complexity of the adapted algorithms. Finally, we show how to remove the dependence on the edge weight in the
In the 4-PATH VERTEX COVER problem, the input is an undirected graph G and an integer k. The goal is to decide whether there is a set S of vertices of size at most k such that every path with 4 vertices in G contains ...
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In the 4-PATH VERTEX COVER problem, the input is an undirected graph G and an integer k. The goal is to decide whether there is a set S of vertices of size at most k such that every path with 4 vertices in G contains at least one vertex of S. In this paper we give a parameterized algorithm for 4-PATH VERTEX COVER whose time complexity is 2.619(k). n(O)(1), where n denotes the number of vertices of the input graph. (C) 2020 Elsevier B.V. All rights reserved.
Timing violations in high performance communication channels in system-on-chips (SoC) may occur in the late stages of the physical design process. To address that, latency insensitive systems (LISs) employ pipelining ...
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Timing violations in high performance communication channels in system-on-chips (SoC) may occur in the late stages of the physical design process. To address that, latency insensitive systems (LISs) employ pipelining in the communication channels through the insertion of relay stations. Although the functionality of an LIS is robust with respect to the communication latencies, imbalances in relay station insertion may degrade the throughput of the system. While having a large number of buffer queues can eliminate such performance loss, the system may not have adequate area to accommodate these buffers. The problem of buffer queue sizing for maximizing throughput while meeting buffer area constraints has been solved using a mixed-integer linear program (MILP) formulation;however, such an approach is not scalable. In this work, we formulate the buffer queue sizing problem as a parameterized graph optimization problem where for every communication channel there is a parameterized edge with buffer counts as the edge weight. We then use a minimum cycle mean algorithm to determine from which edges buffers can be removed safely. Experimental results on large LISs suggest that the proposed approach is scalable. Moreover, quality of the solutions, in terms of the throughput and the size of buffer queues, is observed to be as good as that of the MILP-based approach.
For the last decade, social networking websites have boosted interaction among people through the use of digital communication such as chats, comments, discussion boards and exchange of documentation. This lead to mut...
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For the last decade, social networking websites have boosted interaction among people through the use of digital communication such as chats, comments, discussion boards and exchange of documentation. This lead to mutual learning and sharing of all kind of information. This phenomenon has attracted many researchers and techniques aiming at discovering and prediction links between people have been developed. Most existing solutions are based on the similarity in the profiles using the declared personal information. We present in this paper an approach to discover and predict semantic links between members of a social network based on the content analysis. Our approach uses a textual aggregation function to aggregate keywords extracted from people's textual production. The result of this aggregation is then used to predict semantic links between the members of the network. Experiments were carried out using a real scientific corpus extracted from ReseachGate Web Site. The obtained results showed that our approach, compared to others, achieves better performances in terms of recall, precision, F-measure, complexity and runtime.
We introduce the game of Cops and Eternal Robbers played on graphs, where there are infinitely many robbers that appear sequentially over distinct plays of the game. A positive integer t is fixed, and the cops are req...
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We introduce the game of Cops and Eternal Robbers played on graphs, where there are infinitely many robbers that appear sequentially over distinct plays of the game. A positive integer t is fixed, and the cops are required to capture the robber in at most t time-steps in each play. The associated optimization parameter is the eternal cop number, denoted by c(t)(infinity), which equals the eternal domination number in the case t = 1, and the cop number for sufficiently large t. We study the complexity of Cops and Eternal Robbers, and show that the game is NP-hard when t is a fixed constant and EXPTIME-complete for large values of t. We determine precise values of c(t)(infinity) for paths and cycles. The eternal cop number is studied for retracts, and this approach is applied to give bounds for trees, as well as for strong and Cartesian grids. (C) 2021 Published by Elsevier B.V.
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