Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While ...
详细信息
Distributed processing of large-scale graph data has many practical applications and has been widely studied. In recent years, a lot of distributed graph processing frameworks and algorithms have been proposed. While many efforts have been devoted to analyzing these, with most analyzing them based on programming models, less research focuses on understanding their challenges in distributed environments. Applying graph tasks to distributed environments is not easy, often facing numerous challenges through our analysis, including parallelism, load balancing, communication overhead, and bandwidth. In this article, we provide an extensive overview of the current state-of-the-art in this field by outlining the challenges and solutions of distributed graph algorithms. We first conduct a systematic analysis of the inherent challenges in distributed graph processing, followed by presenting an overview of existing general solutions. Subsequently, we survey the challenges highlighted in recent distributed graph processing papers and the strategies adopted to address them. Finally, we discuss the current research trends and identify potential future opportunities.
With the advent of gated quantum computers and the regular structures for qubit layout, methods for placement, routing, noise estimation, and logic to hardware mapping become imminently required. In this paper, we pro...
详细信息
With the advent of gated quantum computers and the regular structures for qubit layout, methods for placement, routing, noise estimation, and logic to hardware mapping become imminently required. In this paper, we propose a method for quantum circuit layout that is intended to solve such problems when mapping a quantum circuit to a gated quantum computer. The proposed methodology starts by building a Circuit Interaction graph (CIG) that represents the ideal hardware layout minimizing the distance and path length between the individual qubits. The CIG is also used to introduce a qubit noise model. Once constructed, the CIG is iteratively reduced to a given architecture (qubit coupling model) specifying the neighborhood, qubits, priority, and qubits noise. The introduced constraints allow us to additionally reduce the graph according to preferred weights of desired properties. We propose two different methods of reducing the CIG: iterative reduction or the iterative isomorphism search algorithm. The proposed method is verified and tested on a set of standard benchmarks with results showing improvement on certain functions while in average improving the cost of the implementation over the current state of the art methods.
graphs and their algorithms are fundamental to computer science, but they can be difficult to formalise, especially in dependently-typed proof assistants. Part of the problem is that graphs aren't as well-behaved ...
详细信息
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the hea...
详细信息
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.
Modern applications of graph algorithms often involve the use of the output sets (usually, a subset of edges or vertices of the input graph) as inputs to other algorithms. Since the input graphs of interest are large ...
详细信息
Modern applications of graph algorithms often involve the use of the output sets (usually, a subset of edges or vertices of the input graph) as inputs to other algorithms. Since the input graphs of interest are large and dynamic, it is desirable for an algorithm's output to not change drastically when a few random edges are removed from the input graph, so as to prevent issues in postprocessing. Alternately, having such a guarantee also means that one can revise the solution obtained by running the algorithm on the original graph in just a few places in order to obtain a solution for the new graph. We formalize this feature by introducing the notion of average sensitivity of graph algorithms, which is the average earth mover's distance between the output distributions of an algorithm on a graph and its subgraph obtained by removing an edge, where the average is over the edges removed and the distance between two outputs is the Hamming distance. In this work, we initiate a systematic study of average sensitivity of graph algorithms. After deriving basic properties of average sensitivity such as composition, we provide efficient approximation algorithms with low average sensitivities for concrete graph problems, including the minimum spanning forest problem, the global minimum cut problem, the minimum s-t cut problem, and the maximum matching problem. In addition, we prove that the average sensitivity of our global minimum cut algorithm is almost optimal, by showing a nearly matching lower bound. We also show that every algorithm for the 2-coloring problem has average sensitivity linear in the number of vertices. One of the main ideas involved in designing our algorithms with low average sensitivity is the following fact: if the presence of a vertex or an edge in the solution output by an algorithm can be decided locally, then the algorithm has a low average sensitivity, allowing us to reuse the analyses of known sublineartime algorithms and local computation algorith
Designing architectural layouts is a complex task that has garnered significant attention in the research community. While automated site layout design and flat layout design have been extensively studied, automated b...
详细信息
Designing architectural layouts is a complex task that has garnered significant attention in the research community. While automated site layout design and flat layout design have been extensively studied, automated building layout design has been relatively overlooked. This paper describes an approach for generating automated building layouts using deep learning and graph algorithms. A unique building layout dataset is created to support the proposed approach. Euclidean distance, Dice coefficient, and a force-directed graph algorithm are employed for layout selection and fine-tuning. The Input-controlled Spatial Attention U-Net model accurately segments the building region, and the resulting layout is refined through image operations, leading to comprehensive BIM models for designers. Through two generative case studies and a comparative experiment with neural networks, this paper demonstrates the effectiveness of the approach that can assist designers during the initial stages of design and enable a rapid generation of complete layouts for individual buildings.
We study algorithms for the sliding-window model, an important variant of the data-stream model, in which the goal is to compute some function of a fixed-length suffix of the stream. We extend the smooth-histogram fra...
详细信息
We study algorithms for the sliding-window model, an important variant of the data-stream model, in which the goal is to compute some function of a fixed-length suffix of the stream. We extend the smooth-histogram framework of Braverman and Ostrovsky (FOCS 2007) to almost-smooth functions, which includes all subadditive functions. Specifically, we show that if a subadditive function can be (1 + epsilon)-approximated in the insertion-only streaming model, then it can be (2 + epsilon)-approximated also in the sliding-window model with space complexity larger by factor O(epsilon(-1) log omega), where w is the window size. We demonstrate how our framework yields new approximation algorithms with relatively little effort for a variety of problems that do not admit the smooth-histogram technique. For example, in the frequency-vector model, a symmetric norm is subadditive and thus we obtain a sliding-window (2 + epsilon)-approximation algorithm for it. Another example is for streaming matrices, where we derive a new sliding-window (root 2 + epsilon)-approximation algorithm for Schatten 4-norm. We then consider graph streams and show that many graph problems are subadditive, including maximum submodular matching, minimum vertex-cover, and maximum k-cover, thereby deriving sliding-window O(1)-approximation algorithms for them almost for free (using known insertion-only algorithms). Finally, we design for every d is an element of (1, 2] an artificial function, based on the maximum-matching size, whose almost-smoothness parameter is exactly d.
Read-only memory (ROM) model is a classical model of computation to study time-space tradeoffs of algorithms. More recently, several graph algorithms have been studied under ROM model. In this paper, we study graph al...
详细信息
Read-only memory (ROM) model is a classical model of computation to study time-space tradeoffs of algorithms. More recently, several graph algorithms have been studied under ROM model. In this paper, we study graph algorithms under two different relaxations of ROM model, referred to as the implicit and rotate models, and show that these simple relaxations allow us to implement fundamental graph search methods like BFS and DFS more space efficiently than in ROM model. All our algorithms are simple but quite subtle, and we believe that these models are practical enough to spur interest for other graph problems in these models. (C) 2021 The Author(s). Published by Elsevier Inc.
Online graph problems are considered in models where the irrevocability requirement is relaxed. We consider the Late Accept model, where a request can be accepted at a later point, but any acceptance is irrevocable. S...
详细信息
Online graph problems are considered in models where the irrevocability requirement is relaxed. We consider the Late Accept model, where a request can be accepted at a later point, but any acceptance is irrevocable. Similarly, we consider a Late Reject model, where an accepted request can later be rejected, but any rejection is irrevocable (this is sometimes called preemption). Finally, we consider the Late Accept/Reject model, where late accepts and rejects are both allowed, but any late reject is irrevocable. We consider four classical graph problems: For Maximum Independent Set, the Late Accept/Reject model is necessary to obtain a constant competitive ratio, for Minimum Vertex Cover the Late Accept model is sufficient, and for Minimum Spanning Forest the Late Reject model is sufficient. The Maximum Matching problem admits constant competitive ratios in all cases. We also consider Maximum Acyclic Subgraph and Maximum Planar Subgraph, which exhibit patterns similar to Maximum Independent Set.
Tower of Hanoi is a classical tutorial example traditionally for introducing recursive algorithms in CS1. This poster describes a lecture of teaching graph algorithms using the graphical representation of the game and...
详细信息
ISBN:
(纸本)9781450394338
Tower of Hanoi is a classical tutorial example traditionally for introducing recursive algorithms in CS1. This poster describes a lecture of teaching graph algorithms using the graphical representation of the game and its variants in upper-level courses. More specifically, this poster provides a totally different perspective on solving the tower of Hanoi using DFS, BFS, A*, greedy, and other search algorithms on graphs. Students will not only solve the game and its variants in an inspiring way, but also learn a modeling method to convert problems into graphs.
暂无评论