Consider a vertex-weighted graph G with a source s and a target t. TRACKING PATHS requires finding a minimum weight set of vertices (trackers) such that the sequence of trackers in each path from s to t is unique. In ...
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Consider a vertex-weighted graph G with a source s and a target t. TRACKING PATHS requires finding a minimum weight set of vertices (trackers) such that the sequence of trackers in each path from s to t is unique. In this work, we derive a factor 6-approximation algorithm for TRACKING PATHS in weighted graphs and a factor 4-approximation algorithm if the input is unweighted. This is the first constant factor approximation for this problem. While doing so, we also study approximation of the closely related r-FAULT TOLERANT FEEDBACK VERTEX SET problem. There, for a fixed integer r and a given vertex-weighted graph G, the task is to find a minimum weight set of vertices intersecting every cycle of G in at least r + 1 vertices. We give a factor O(r) approximation algorithm for r-FAULT TOLERANT FEEDBACK VERTEX SET if r is a constant.(c) 2022 Elsevier B.V. All rights reserved.
We present the file search problem in a decision-theoretic framework, and discuss a variation of it that we call the common index problem. The goal of the common index problem is to return the best available record in...
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We present the file search problem in a decision-theoretic framework, and discuss a variation of it that we call the common index problem. The goal of the common index problem is to return the best available record in the file, where best is in terms of a class of user preferences. We use dynamic programming to construct an optimal algorithm using two different optimality criteria, and we develop sufficient conditions for obtaining complete information.
The minimum-weight perfect matching problem for complete graphs of n vertices with edge weights satisfying the triangle inequality is considered. For each nonnegative integer k ≤ log3n, and for any perfect matching a...
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The minimum-weight perfect matching problem for complete graphs of n vertices with edge weights satisfying the triangle inequality is considered. For each nonnegative integer k ≤ log3n, and for any perfect matching algorithm that runs in t(n) time and has an error bound of ƒ(n) times the optimal weight, an O(max{n2, t(3-kn)})-time heuristic algorithm with an error bound of (7/3)k(1 + ƒ(3 kn)) - 1 is given. By the selection of k as appropriate functions of n, heuristics that have better running times and/or error bounds than existing ones are derived.
The triangle counting problem in graph streams has been extensively studied in social network analysis, recommendation systems, user portraits and other fields. However, cloud computing based streaming algorithms caus...
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The triangle counting problem in graph streams has been extensively studied in social network analysis, recommendation systems, user portraits and other fields. However, cloud computing based streaming algorithms cause high bandwidth occupation and long transmission latency due to limited bandwidth of the cloud. Recently, edge computing is promising to overcome the issue of transmitting large-scale data for cloud computing. However, directly applying edge computing in streaming triangle counting will reduce the accuracy of the triangle count estimation, due to the limitation of local computing at the edge network. We term the cooperations between edge computing and cloud computing for streaming triangle counting as edge-cloud triangle counting in graph streams. In this paper, we first propose a streaming framework for edge-cloud triangle counting in graph streams. Then, we propose a streaming triangle counting algorithm called Trie-based Edge Compression (TbEC) by using the binary trie at the edge network that enables lossless compression and efficient transmission to the cloud. In addition, to extend our algorithms for triangle counting in multigraphs, we present a dual deduplication strategy collaboratively using the trie-based data structure and a Bloom Filter. Our experiments with real-world datasets show that TbEC is (a) Accurate: yielding up to 3.35x more accurate smaller estimation error than the state-of-the-art distributed streaming algorithm, (b) Fast: yielding up to 10.59x faster than the state-of-the-art distributed streaming algorithm, (c) Scalable: scaling linearly with the number of edges in the input graph stream.& COPY;2023 Elsevier B.V. All rights reserved.
This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only a local access to a subset of a state vector information as often ...
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This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only a local access to a subset of a state vector information as often encountered in decentralized control problems in multi-agent systems. Under this information structure, part of the state vector cannot be observed. We leverage ab initio principles and find a new form of Bellman equations to characterize the optimal policies of the control problem under local information structures. The dynamic programming solutions feature a mixture of dynamics associated unobservable state components and the local state-feedback policy based on the observable local information. We further characterize the optimal local-state feedback policy using linear programming methods. To reduce the computational complexity of the optimal policy, we propose an approximate algorithm based on virtual beliefs to find a sub-optimal policy. We show the performance bounds on the sub-optimal solution and corroborate the results with numerical case studies.
Data stream processing plays a critical role in providing fundamental statistics for various applications, such as anomaly detection. Still, the unbalanced distribution of data streams severely affects the performance...
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Data stream processing plays a critical role in providing fundamental statistics for various applications, such as anomaly detection. Still, the unbalanced distribution of data streams severely affects the performance of related algorithms, which motivates the recent studies on filter structure design to enhance the existing algorithms for a more precise estimation result. However, these filters are mainly designed for frequency-based filtration, while none of them can conduct universal filtration;apparently, frequency is not the only targeted metric in practical processing tasks, metrics like cardinality and persistence are of equal importance. To cope with the issue, we propose a novel filter framework to implement universal, lightweight, and accurate filtration. The filter framework is called Coupon Filter due to the interpretation of its flow-level filtration as a coupon collection process. We prove the filtration efficiency of our filter design and formally analyze its recording process. We deploy our filter on the three typical metrics (frequency, cardinality, and persistence) to illustrate its advantages. The experimental results on real Internet traces demonstrate the effectiveness of our filter in enhancing existing stream processing approaches in terms of accuracy and throughput. All source codes are available at Github https://***/duyang92/coupon-filter-paper.
Recently there has been significant interest in applications of game-theoretic analysis to analyze security resource allocation decisions. Two examples of deployed systems based on this line of research are the ARMOR ...
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ISBN:
(纸本)9783642171963
Recently there has been significant interest in applications of game-theoretic analysis to analyze security resource allocation decisions. Two examples of deployed systems based on this line of research are the ARMOR system in use at the Los Angeles International Airport [20], and the IRIS system used by the Federal Air Marshals Service [25]. Game analysis always begins by developing a model of the domain, often based on inputs from domain experts or historical data. These models inevitably contain significant uncertainty--especially in security domains where intelligence about adversary capabilities and preferences is very difficult to gather. In this work we focus on developing new models and algorithms that capture this uncertainty using continuous payoff distributions. These models are richer and more powerful than previous approaches that are limited to small finite Bayesian game models. We present the first algorithms for approximating equilibrium solutions in these games, and study these algorithms empirically. Our results show dramatic improvements over existing techniques, even in cases where there is very limited uncertainty about an adversaries' payoffs.
In this paper we investigate an approach to provide approximate, anytime algorithms for DCOPs that can provide quality guarantees. At this aim, we propose the divide-and-coordinate (DaC) approach. Such approach amount...
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ISBN:
(纸本)9780982657119
In this paper we investigate an approach to provide approximate, anytime algorithms for DCOPs that can provide quality guarantees. At this aim, we propose the divide-and-coordinate (DaC) approach. Such approach amounts to solving a DCOP by iterating (1) a divide stage in which agents divide the DCOP into a set of simpler local subproblems and solve them; and (2) a coordinate stage in which agents exchange local information that brings them closer to an agreement. Next, we formulate a novel algorithm, the Divide and Coordinate Subgradient Algorithm (DaCSA), a computational realization of DaC based on Lagrangian decompositions and the dual subgradient method. By relying on the DaC approach, DaCSA provides bounded approximate solutions. We empirically evaluate DaCSA showing that it is competitive with other state-of-the-art DCOP approximate algorithms and can eventually outper-form them while providing useful quality guarantees.
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