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UID:8353@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20140523T140000
DTEND;TZID=Europe/Paris:20140523T150000
DTSTAMP:20241120T210346Z
URL:https://www.i2m.univ-amu.fr/evenements/p-borgnat-ens-lyon-graph-wavele
 ts-and-multiscale-community-mining-in-networks/
SUMMARY: (...): P. Borgnat (ENS Lyon): Graph Wavelets and Multiscale Commun
 ity Mining in networks
DESCRIPTION:: Graph Wavelets and Multiscale Community Mining in networks\n\
 nBy Pierre Borgnat\\\, ENS Lyon\n\nJoint work with N. Tremblay\n\nFor netw
 orks\\\, an important issue is the finding of communities\\\, i.e.\\\, gro
 ups of nodes that are well connected together\\\, and more than with the r
 est of the network. A signal processing approach is developed for the mult
 iscale detection of communities in networks. This method relies on a caref
 ully engineered wavelet transform on graphs\\\, so as to introduce the not
 ion of scale and to obtain a local view of the graph from each node. This 
 gives a notion of distance between nodes\\\, thereby enabling to cluster n
 odes according to their community at the scale of analysis. To make the me
 thod suitable for the analysis of large graphs\\\, a collection of random 
 vectors is used to estimate the correlation between the nodes. Finally\\\,
  a notion of partition stability and an empirical statistical test are int
 roduced\\\, allowing us to assess which scales of analysis of the network 
 are relevant. The method is illlustrated on real data of social networks\\
 \, on models for signal processing on graphs\\\, and on benchmarks of grap
 hs with multiscale communities.
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DTSTART:20140330T030000
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