Results 1  10
of
50
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
Abstract

Cited by 821 (1 self)
 Add to MetaCart
The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such
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 ..."
Abstract

Cited by 59 (4 self)
 Add to MetaCart
(Show Context)
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.
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 ..."
Abstract

Cited by 57 (13 self)
 Add to MetaCart
(Show Context)
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.
Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
"... ..."
Community Structure in Graphs
, 2007
"... Graph vertices are often organized into groups that seem to live fairly independently of the rest of the graph, with which they share but a few edges, whereas the relationships between group members are stronger, as shown by the large number of mutual connections. Such groups of vertices, or communi ..."
Abstract

Cited by 44 (0 self)
 Add to MetaCart
Graph vertices are often organized into groups that seem to live fairly independently of the rest of the graph, with which they share but a few edges, whereas the relationships between group members are stronger, as shown by the large number of mutual connections. Such groups of vertices, or communities, can be considered as independent compartments of a graph. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. The task is very hard, though, both conceptually, due to the ambiguity in the definition of community and in the discrimination of different partitions and practically, because algorithms must find “good ” partitions among an exponentially large number of them. Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to the presence of nodes belonging to more groups. All these aspects are dealt with in some detail and many methods are described, from traditional approaches used in computer science and sociology to recent techniques developed mostly within statistical physics.
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 ..."
Abstract

Cited by 36 (2 self)
 Add to MetaCart
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 ..."
Abstract

Cited by 32 (4 self)
 Add to MetaCart
(Show Context)
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
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 ..."
Abstract

Cited by 20 (1 self)
 Add to MetaCart
(Show Context)
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...
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 ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
(Show Context)
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.