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36
Graph partitioning for high performance scientific simulations. Computing Reviews 45(2
, 2004
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Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
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Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
 Evolutionary Computation
, 2000
"... The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis ..."
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Cited by 48 (13 self)
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The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced.
An Energy Model for Visual Graph Clustering
 Proceedings of the 11th International Symposium on Graph Drawing (GD 2003), LNCS 2912
, 2003
"... We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose ..."
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Cited by 41 (4 self)
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We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose diameter is small relative to the number of nodes. We formally characterize the minimum energy drawings of our energy model. This characterization shows in what sense the drawings separate clusters, and how the distance of separated clusters to the other nodes can be interpreted.
Visual Clustering of Graphs with Nonuniform Degrees
 Proceedings of the 13th International Symposium on Graph Drawing (GD 2005
, 2004
"... We discuss several criteria for clustering graphs, and identify two criteria which are not biased towards certain cluster sizes: the nodenormalized cut (also called cut ratio) and the edgenormalized cut. We present two energy models whose minimum energy drawings reveal clusters with respect to ..."
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Cited by 27 (2 self)
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We discuss several criteria for clustering graphs, and identify two criteria which are not biased towards certain cluster sizes: the nodenormalized cut (also called cut ratio) and the edgenormalized cut. We present two energy models whose minimum energy drawings reveal clusters with respect to these criteria.
Metricsbased 3d visualization of large objectoriented programs
, 2002
"... Software belongs to the most complex humanmade artefacts. The size and complexity of programs has constantly grown over the last years. Today in many application domains (e.g. ebusiness, switching systems) software systems with millions of lines of code are constructed. They consist of many thousa ..."
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Cited by 22 (5 self)
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Software belongs to the most complex humanmade artefacts. The size and complexity of programs has constantly grown over the last years. Today in many application domains (e.g. ebusiness, switching systems) software systems with millions of lines of code are constructed. They consist of many thousands
DiSenS: Scalable Distributed Sensor Network Simulation
 In Proceedings of ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 07
, 2005
"... Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make tradeoffs between fi ..."
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Cited by 13 (5 self)
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Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make tradeoffs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) – a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributedmemory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (1664 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in subrealtime speed. To our knowledge, DiSenS is the first fullsystem sensor network simulator with such scalability.
Hierarchical eigensolver for transition matrices in spectral methods
 NIPS
, 2005
"... We show how to build hierarchical, reducedrank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest eigenvalues, and the corresponding eigenvectors. In particular, the eigen problem is first solved at the coarsest level ..."
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Cited by 12 (2 self)
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We show how to build hierarchical, reducedrank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest eigenvalues, and the corresponding eigenvectors. In particular, the eigen problem is first solved at the coarsest level of the representation. The approximate eigen solution is then interpolated over successive levels of the hierarchy. A small number of power iterations are employed at each stage to correct the eigen solution. The typical speedups obtained by a Matlab implementation of our fast eigensolver over a standard sparse matrix eigensolver [13] are at least a factor of ten for large image sizes. The hierarchical representation has proven to be effective in a mincut based segmentation algorithm that we proposed recently [8]. 1 Spectral Methods Graphtheoretic spectral methods have gained popularity in a variety of application domains:
Graph Partitioning Algorithms for Distributing Workloads of Parallel Computations
, 1998
"... This paper surveys graph partitioning algorithms used for parallel computing, with an emphasis on the problem of distributing workloads for parallel computations. Geometric, structural, and refinementbased algorithms are described and contrasted. In addition, multilevel partitioning techniques and ..."
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Cited by 12 (1 self)
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This paper surveys graph partitioning algorithms used for parallel computing, with an emphasis on the problem of distributing workloads for parallel computations. Geometric, structural, and refinementbased algorithms are described and contrasted. In addition, multilevel partitioning techniques and issues related to parallel partitioning are addressed. All algorithms are evaluated qualitatively in terms of their execution speed and ability to generate partitions with small separators. 1 Introduction In its most general form, the graph partitioning problem asks how best to divide a graph's vertices into a specified number of subsets such that: (i) the number of vertices per subset is equal and (ii) the number of edges straddling the subsets is minimized. Graph partitioning has several important applications in Computer Science, including VLSI circuit layout [8], image processing [43], solving sparse linear systems, computing fillreducing orderings for sparse matrices, and distribu...
Halflives of eigenflows for spectral clustering
 Advances in Neural Information Processing Systems 689–696
, 2002
"... Using a Markov chain perspective of spectral clustering we present an algorithm to automatically find the number of stable clusters in a dataset. The Markov chain’s behaviour is characterized by the spectral properties of the matrix of transition probabilities, from which we derive eigenflows along ..."
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Cited by 10 (1 self)
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Using a Markov chain perspective of spectral clustering we present an algorithm to automatically find the number of stable clusters in a dataset. The Markov chain’s behaviour is characterized by the spectral properties of the matrix of transition probabilities, from which we derive eigenflows along with their halflives. An eigenflow describes the flow of probability mass due to the Markov chain, and it is characterized by its eigenvalue, or equivalently, by the halflife of its decay as the Markov chain is iterated. A ideal stable cluster is one with zero eigenflow and infinite halflife. The key insight in this paper is that bottlenecks between weakly coupled clusters can be identified by computing the sensitivity of the eigenflow’s halflife to variations in the edge weights. We propose a novel EIGENCUTS algorithm to perform clustering that removes these identified bottlenecks in an iterative fashion. 1