We prove that the rank-width of an n-vertex graph can be computed exactly in time O(2(n)n(3) log(2) n log log n). To improve over a trivial O(3(n) log n)-time algorithm, we develop a general framework for decompositio...
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We prove that the rank-width of an n-vertex graph can be computed exactly in time O(2(n)n(3) log(2) n log log n). To improve over a trivial O(3(n) log n)-time algorithm, we develop a general framework for decompositions on which an optimal decomposition can be Computed efficiently. This framework may be used for other width parameters, including the branch-width of matroids and the carving-width of graphs. (C) 2009 Elsevier B.V. All rights reserved.
in many applications we are required to increase the deployment of a distributed monitoring system on an evolving network. In this paper we present a new method for finding candidate locations for additional deploymen...
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in many applications we are required to increase the deployment of a distributed monitoring system on an evolving network. In this paper we present a new method for finding candidate locations for additional deployment in the network. This method is based on the Group Betweenness Centrality (GBC) measure that is used to estimate the influence of a group of nodes over the information flow in the network. The new method assists in finding the location of k additional monitors in the evolving network, such that the portion of additional traffic covered is at least (1 - 1/e) of the optimal. (c) 2009 Elsevier B.V. All rights reserved.
The single maximum coverage location problem on a tree consists of placing one facility onto a node such that the total weight of users covered by the facility is maximized. Users are located at nodes of the tree. Eac...
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The single maximum coverage location problem on a tree consists of placing one facility onto a node such that the total weight of users covered by the facility is maximized. Users are located at nodes of the tree. Each user has an individual sensitivity radius and is covered if and only if the facility is placed within that radius. This problem is a generalization of the (1, X-p)-medianoid problem (Hakimi, 1990). We provide a recursive graph coarsening algorithm which improves the running time from O(n(2)) (Megiddo, 1983) to 0 (n(logn)(2)/loglogn). (C) 2008 Elsevier B.V. All rights reserved.
We give a randomized algorithm that determines if a given graph has a simple path of length at least k in O(2(k) . poly(n)) time. Our method extends a recent O(2(3k/2). poly(n)) <= 0 (2.83(k) . poly(n)) algorithm o...
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We give a randomized algorithm that determines if a given graph has a simple path of length at least k in O(2(k) . poly(n)) time. Our method extends a recent O(2(3k/2). poly(n)) <= 0 (2.83(k) . poly(n)) algorithm of Koutis. (C) 2008 Elsevier B.V. All rights reserved.
Given an edge-weighted tree T = (V(T), E(T)) and its subtree T', for any v is an element of V(T), the distance d(v, T') is defined as the minimum weighted distance from v to any vertex in T'. The distance ...
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Given an edge-weighted tree T = (V(T), E(T)) and its subtree T', for any v is an element of V(T), the distance d(v, T') is defined as the minimum weighted distance from v to any vertex in T'. The distance d(T, T') is defined as the sum of all distances of the form d(v, T') where v is an element of V(T). We give an algorithm to find a T' that minimizes d(T, T') and for all w is an element of V(T'), the degree deg(T')(w) of w is not more than a prespecified bound db(w) (0 <= db(w) <= deg(T)(w)) at w. The worst-case running time of our algorithm is in O(vertical bar V(T)vertical bar). (C) 2009 Elsevier B.V. All rights reserved.
In this paper, we extend the notion of similarity matrix, which has been used to define similarity between nodes of two graphs, to the case of colored graphs, where the coloring is either on the nodes or on the edges ...
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In this paper, we extend the notion of similarity matrix, which has been used to define similarity between nodes of two graphs, to the case of colored graphs, where the coloring is either on the nodes or on the edges of both graphs. The proposed method tries to find the optimal matching between the nodes or edges of both graphs but only performs the comparison when their colors are the same. The proposed cost function nevertheless uses the connectivity between all nodes and edges of both graphs. We then also show how to extend this to the notion of low rank similarity matrix, by defining it as a constrained optimization problem.
Wavelet FIR filter architecture using Pipe lined Addition Reordering (PAR) technique is designed and presented in this paper. The average adder cost and computation time for the proposed PAR algorithm is compared with...
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ISBN:
(纸本)9780769538921
Wavelet FIR filter architecture using Pipe lined Addition Reordering (PAR) technique is designed and presented in this paper. The average adder cost and computation time for the proposed PAR algorithm is compared with other existing graphical algorithms. Experimental results show that PAR algorithm reduces the hardware cost by 14% compared to RAG-n (Reduced Adder graph) and 17% compared to BIM (Modified Bull and Horrocks) algorithm. The speed of the circuit is found to increase by a factor of 3.7 when compared to ordinary addition reordering technique. The proposed algorithm is significant due to the increased throughput, low average power dissipation and reduced latency.
We consider distributed algorithms for approximate maximum matching on general graphs. Our main result is a randomized (4 + epsilon)-approximation distributed algorithm for maximum weighted matching, whose running tim...
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We consider distributed algorithms for approximate maximum matching on general graphs. Our main result is a randomized (4 + epsilon)-approximation distributed algorithm for maximum weighted matching, whose running time is O(log n) for any constant epsilon > 0, where n is the number of nodes in the graph. This is, to the best of our knowledge, the first log-time distributed algorithm that achieves constant approximation for maximum weighted matching on general graphs. In addition, we consider the dynamic case, where nodes are inserted and deleted one at a time. For unweighted dynamic graphs, we give a distributed algorithm that maintains a (1 + epsilon)-approximation in O(1/epsilon) time for each node insertion or deletion for any constant epsilon > 0. For weighted dynamic graphs we give a constant-factor approximation distributed algorithm that runs in constant time for each insertion or deletion.
One of the fundamental assumptions for machine-learning based text classification systems is that the underlying distribution from which the set of labeled-text is drawn is identical to the distribution from which the...
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ISBN:
(纸本)9789896740115
One of the fundamental assumptions for machine-learning based text classification systems is that the underlying distribution from which the set of labeled-text is drawn is identical to the distribution from which the text-to-be-labeled is drawn. However, in live news aggregation sites, this assumption is rarely correct. Instead, the events and topics discussed in news stories dramatically change over time. Rather than ignoring this phenomenon, we attempt to explicitly model the transitions of news stories and classifications over time to label stories that may be acquired months after the initial examples are labeled. We test our system, based on efficiently propagating labels in time-based graphs, with recently published news stories collected over an eighty day period. Experiments presented in this paper include the use of training labels from each story within the first several days of gathering stories, to using a single story as a label.
Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We ...
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ISBN:
(纸本)9781605584959
Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the TEAM FORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the TEAM FORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.
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