10 research outputs found
Overlapping Community Detection in Networks: the State of the Art and Comparative Study
This paper reviews the state of the art in overlapping community detection
algorithms, quality measures, and benchmarks. A thorough comparison of
different algorithms (a total of fourteen) is provided. In addition to
community level evaluation, we propose a framework for evaluating algorithms'
ability to detect overlapping nodes, which helps to assess over-detection and
under-detection. After considering community level detection performance
measured by Normalized Mutual Information, the Omega index, and node level
detection performance measured by F-score, we reached the following
conclusions. For low overlapping density networks, SLPA, OSLOM, Game and COPRA
offer better performance than the other tested algorithms. For networks with
high overlapping density and high overlapping diversity, both SLPA and Game
provide relatively stable performance. However, test results also suggest that
the detection in such networks is still not yet fully resolved. A common
feature observed by various algorithms in real-world networks is the relatively
small fraction of overlapping nodes (typically less than 30%), each of which
belongs to only 2 or 3 communities.Comment: This paper (final version) is accepted in 2012. ACM Computing
Surveys, vol. 45, no. 4, 2013 (In press) Contact: [email protected]
Comparative gustatory responses in four species of gerbilline rodents
Integrated taste responses to chemical stimulation of the tongue were recorded from the intact chorda tympani nerve in four species of gerbils ( Meriones libycus, M. shawi, M. unguiculatus and Psammomys obesus ).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47116/1/359_2004_Article_BF00618177.pd
Comparison of methods for the detection of node group membership in bipartite networks
Most real-world networks considered in the literature have
a modular structure. Analysis of these real-world networks often are
performed under the assumption that there is only one type of
node. However, social and biochemical systems are often bipartite
networks, meaning that there are two exclusive sets of nodes, and
that edges run exclusively between nodes belonging to different
sets. Here we address the issue of module detection in bipartite
networks by comparing the performance of two classes of group
identification methods â modularity maximization and clique
percolation â on an ensemble of modular random bipartite
networks. We find that the modularity maximization methods are able
to reliably detect the modular bipartite structure, and that, under
some conditions, the simulated annealing method outperforms the
spectral decomposition method. We also find that the clique
percolation methods are not capable of reliably detecting the
modular bipartite structure of the bipartite model networks
considered
Finding Modules in Networks with Non-modular Regions
Abstract. Most network clustering methods share the assumption that the network can be completely decomposed into modules, that is, every node belongs to (usually exactly one) module. Forcing this constraint can lead to misidentification of modules where none exist, while the true modules are drowned out in the noise, as has been observed e. g. for protein interaction networks. We thus propose a clustering model where networks contain both a modular region consisting of nodes that can be partitioned into modules, and a transition region containing nodes that lie between or outside modules. We propose two scores based on spectral properties to determine how well a network fits this model. We then evaluate three (partially adapted) clustering algorithms from the literature on random networks that fit our model, based on the scores and comparison to the ground truth. This allows to pinpoint the types of networks for which the different algorithms perform well.