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Help
If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.
Customer service
All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com
Sale & Press
If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com
Dans de nombreux contextes, des ensembles d'entits en relation peuvent tre modliss par des graphes, dans lesquels les entits individuelles sont reprsentes par des sommets et les relations entre ces entits par des liens. Ces graphes, que nous appellerons "graphes de terrain", peuvent tre rencontrs dans le monde rel dans diffrents domaines tels que les sciences sociales, l'informatique, la biologie, le transport, la linguistique, etc. La plupart des graphes de terrain sont composs de sousgraphes denses faiblement interconnects appels communauts et de nombreux algorithmes ont t proposs afin d'identifier cette structure communautaire automatiquement. Nous nous sommes intresss aux problmes des algorithmes de dtection de communauts, notamment leur nondterminisme et l'instabilit qui en dcoule. Nous avons prsent une mthodologie qui permets d'amliorer les rsultats obtenus avec les techniques actuelles de dtection de communauts. Nous avons propos une approche base sur le concept de communauts fortes ou curs de communauts et nous avons montr l'amlioration apporte par notre approche en l'appliquant des graphes rels et artificiels.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer,
birth defects, or other reproductive harm.
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.
