Article
On Modularity Clustering
Univ. of Konstanz, Konstanz
IEEE Transactions on Knowledge and Data Engineering (Impact Factor: 1.89). 03/2008; DOI: 10.1109/TKDE.2007.190689 Source: IEEE Xplore

Article: Optimal Behavioral Hierarchy.
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ABSTRACT: Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goaldirected activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies.PLoS Computational Biology 08/2014; 10(8):e1003779. · 4.87 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The amount of graphstructured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism will be necessary to scale to the data volume of realworld applications. We address the deficit in computing capability by a flexible and extensible community detection framework with sharedmemory parallelism. Within this framework we design and implement efficient parallel community detection heuristics: A parallel label propagation scheme; the first largescale parallelization of the wellknown Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. We also compare our implementations with state of the art competitors. The processing rate of our fastest algorithm often reaches 50M edges/second, making it suitable for massive data sets with billions of edges. We recommend the parallel Louvain method and our variant with refinement as both qualitatively strong and fast.07/2014; 
Conference Paper: Detecting Communities Around Seed Nodes in Complex Networks
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ABSTRACT: The detection of communities (internally dense subgraphs) is a network analysis task with manifold applications. The special task of selective community detection is concerned with finding highquality communities locally around seed nodes. Given the lack of conclusive experimental studies, we perform a systematic comparison of different previously published as well as novel methods. In particular we evaluate their performance on large complex networks, such as social networks. Algorithms are compared with respect to accuracy in detecting ground truth communities, community quality measures, size of communities and running time. We implement a generic greedy algorithm which subsumes several previous efforts in the field. Experimental evaluation of multiple objective functions and optimizations shows that the frequently proposed greedy approach is not adequate for large datasets. As a more scalable alternative, we propose selSCAN, our adaptation of a global, densitybased community detection algorithm. In a novel combination with algebraic distances on graphs, query times can be strongly reduced through preprocessing. However, selSCAN is very sensitive to the choice of numeric parameters, limiting its practicality. The randomwalkbased PageRankNibble emerges from the comparison as the most successful candidate.IEEE BigData '14  BigGraphs Workshop; 10/2014
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