Life-Cycles and Mutual Effects of Scientific Communities

@article{Belk2010LifeCyclesAM,
  title={Life-Cycles and Mutual Effects of Scientific Communities},
  author={V{\'a}clav Bel{\'a}k and Marcel Karnstedt and Conor Hayes},
  journal={ArXiv},
  year={2010},
  volume={abs/1010.4327}
}
Abstract Cross-community e_ects on the behaviour of individuals and communities themselves can be observed in a wide range of applications. While previous work has tried to explain and analyse such phenomena, there is still a great potential for increasing the quality and accuracy of this analysis. In this work, we propose a general framework consisting of several di_erent techniques to analyse and explain cross-community e_ects and the underlying dynamics. The proposed methodology works with… Expand

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References

SHOWING 1-10 OF 45 REFERENCES
Towards Cross-Community Effects in Scientific Communities
TLDR
This work chooses two closely related communities and proposes novel ideas to detect and explain cross-community effects with a special focus on their characteristics in a given timeline, which promises valuable insights for all areas of scientific research. Expand
Stability of graph communities across time scales
TLDR
The stability of a partition is introduced, a measure of its quality as a community structure based on the clustered autocovariance of a dynamic Markov process taking place on the network, and the dynamical definition provides a unifying framework for several standard partitioning measures. Expand
Quantifying social group evolution
TLDR
The focus is on networks capturing the collaboration between scientists and the calls between mobile phone users, and it is found that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. Expand
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. Expand
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
TLDR
This paper proposes FacetNet, a novel framework for analyzing communities and their evolutions through a robust unified process, where communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. Expand
Tracking the Evolution of Communities in Dynamic Social Networks
TLDR
A model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Expand
Community detection in graphs
TLDR
A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists. Expand
Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science, Physics
  • 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. Expand
A framework for community identification in dynamic social networks
TLDR
It is proved that finding the most explanatory community structure is NP-hard and APX-hard, and it is demonstrated empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples. Expand
GraphScope: parameter-free mining of large time-evolving graphs
TLDR
The efficiency and effectiveness of the GraphScope is demonstrated, which is designed to operate on large graphs, in a streaming fashion, on real datasets from several diverse domains, and produces meaningful time-evolving patterns that agree with human intuition. Expand
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4
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