Criterions for locally dense subgraphs

@article{Tibly2011CriterionsFL,
  title={Criterions for locally dense subgraphs},
  author={Gergely Tib{\'e}ly},
  journal={ArXiv},
  year={2011},
  volume={abs/1103.3397}
}
  • G. Tibély
  • Published 17 March 2011
  • Computer Science
  • ArXiv

Figures and Tables from this paper

An Empirical Study on Community Detection Algorithms
TLDR
Different community detection algorithms are presented and their pros and cons are discussed and some of the research challenges in this area are stated.
Analysis of community-detection methods based on Potts spin model in complex networks
TLDR
A critical analysis of the multiscale methods based on Potts spin model for community detection are described and compared in the analysis of community structures of several networks, showing a kind of limitation that the methods may suffer from when the community size difference is very broad.
Bad communities with high modularity
TLDR
The overestimation effect of Newman and Girvan’s modularity function QN is illustrated by constructing families of graphs with a “natural” community structure which, however, does not maximize modularity.
Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches
TLDR
Three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs are implemented and assessed and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources.
Are motorways rational from slime mould's point of view?
TLDR
It is found that the slime mould approximates best of all the motorway graphs of Belgium, Canada and China, and that for all entities studied the best match between Physarum andMotorway graphs is detected by the Randić index (molecular branching index).

References

SHOWING 1-10 OF 172 REFERENCES
Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.
TLDR
The basic ideas behind the previous benchmark are extended to generate directed and weighted networks with built-in community structure, and the possibility that nodes belong to more communities is considered, a feature occurring in real systems, such as social networks.
Finding Statistically Significant Communities in Networks
TLDR
OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.
Benchmark graphs for testing community detection algorithms.
TLDR
This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.
Detecting highly overlapping community structure by greedy clique expansion
TLDR
GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities, and when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, the algorithm performs competitively.
Defining and identifying communities in networks.
TLDR
This article proposes a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability and applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods.
Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
TLDR
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Statistical significance of communities in networks.
TLDR
A measure aimed at quantifying the statistical significance of single communities is defined, which is successfully applied in the case of real-world networks for the evaluation of the significance of their communities.
Identifying network communities with a high resolution.
TLDR
An efficient heuristic algorithm QCUT, which combines spectral graph partitioning and local search to optimize Q is proposed and it is shown that QCUT can find higher modularities and is more scalable than the existing algorithms.
Resolution limit in community detection
TLDR
It is found that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined.
Community detection algorithms: a comparative analysis.
TLDR
Three recent algorithms introduced by Rosvall and Bergstrom and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
...
...