Interpreting communities based on the evolution of a dynamic attributed network

@article{Orman2015InterpretingCB,
  title={Interpreting communities based on the evolution of a dynamic attributed network},
  author={G{\"u}nce Keziban Orman and Vincent Labatut and Marc Plantevit and Jean-François Boulicaut},
  journal={Social Network Analysis and Mining},
  year={2015},
  volume={5},
  pages={1-22}
}
AbstractMany methods have been proposed to detect communities, not only in plain, but also in attributed, directed, or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the… 
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References

SHOWING 1-10 OF 52 REFERENCES
Detection and Interpretation of Communities in Complex Networks: Practical Methods and Application
TLDR
This article first review community detection algorithms and characterize them in terms of the nature of the communities they detect, then focuses on the methodological tools one can use to analyze the obtained community structure, both in termsof topological features and nodal attributes.
The Effect of Network Realism on Community Detection Algorithms
  • G. Orman, Vincent Labatut
  • Computer Science
    2010 International Conference on Advances in Social Networks Analysis and Mining
  • 2010
TLDR
This work proposes a modification of Lancichinetti et al.'s approach, based on the preferential attachment model, to produce realistic networks, with a community structure and power law distributed degrees and community sizes, but other realistic properties such as degree correlation and transitivity are missing.
Community detection algorithms: a comparative analysis: invited presentation, extended abstract
TLDR
Three recent algorithms introduced by Rosvall and Bergstrom, Blondel et al. and Ronhovde and Nussinov have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.
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.
Community detection in graphs
Characterizing the Community Structure of Complex Networks
TLDR
A systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks finds that the mesoscopic organization of networks of the same category is remarkably similar.
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 2002
TLDR
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Statistical properties of community structure in large social and information networks
TLDR
It is found that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to that observed in nearly every network dataset examined.
Community Detection in Networks with Node Attributes
TLDR
This paper develops Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes that statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in thenetwork structure.
Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions. (Contribution à l'interprétation des communautés en évolution dans des réseaux complexes : Application à l'étude des interactions sociales)
TLDR
This thesis proposes a new representation of communities under the form of temporal sequences of topological measures and attribute values associated to individual nodes, and looks for emergent sequential patterns in this dataset, in order to identify the most characteristic community features.
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