Personnaliser

OK
Rakuten - Achat et vente en ligne de produits neufs et d'occasionRakuten group
ClubR
Euro

Mettre en vente

Person

Se connecter

Heart
Cart
Rakuten - Achat et vente en ligne de produits neufs et d'occasionRakuten group
ClubR
Person

Se connecter

Cart

Graph Data Mining -

Note : 0

0 avis
  • Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

206,59 €

Produit Neuf

  • Ou 51,65 € /mois

    • Livraison à 0,01 €
    • Livré entre le 4 et le 11 avril
    Voir les modes de livraison

    RiaChristie

    PRO Vendeur favori

    4,9/5 sur + de 1 000 ventes

    Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9789811626081_dbm

    Publicité
     
    Vous avez choisi le retrait chez le vendeur à
    • Payez directement sur Rakuten (CB, PayPal, 4xCB...)
    • Récupérez le produit directement chez le vendeur
    • Rakuten vous rembourse en cas de problème

    Gratuit et sans engagement

    Félicitations !

    Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

    En savoir plus

    Retour

    Horaires

        Note :


        Avis sur Graph Data Mining de Format Relié  - Livre Informatique

        Note : 0 0 avis sur Graph Data Mining de Format Relié  - Livre Informatique

        Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.


        Présentation Graph Data Mining de Format Relié

         - Livre Informatique

        Livre Informatique - 01/07/2021 - Relié - Langue : Anglais

        . .

      • Editeur : Springer Singapore
      • Langue : Anglais
      • Parution : 01/07/2021
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 260
      • Expédition : 559
      • Dimensions : 24.1 x 16.0 x 2.0
      • ISBN : 9811626081



      • Résumé :
        Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic - the security of graph data mining - and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains. ...

        Biographie:

        Qi Xuan is a Professor at the Institute of Cyberspace Security,  Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, graph data mining, cyberspace security, and deep learning. He has published more than 50 papers in leading journals and conferences, including IEEE TKDE, IEEE TIE, IEEE TNSE, ICSE, and FSE. He is the reviewer of the journals such like IEEE TKDE, IEEE TIE, IEEE TII, and IEEE TNSE.

        Zhongyuan Ruan is a lecturer at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His current research interests include network science, such as epidemic and information spreading in complex networks, and traffic networks. He has published more than 20 papers in journals such as Physical Review Letters, Physical Review E, Chaos, Scientific Reports, and Physica A.

        Yong Min is an Associate Professor at the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China. His research interests include social network analysis, computational communication, and artificial intelligence algorithms. He was named an Excellent Young Teacher of Zhejiang University of Technology. He has hosted and participated in more than ten projects, including those by national and provincial natural science foundations. He has also published over 30 papers, including two in the leading journal Nature and Science, and he holds more than three patents.

        Sommaire:
        Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world applications in fields such as bioinformatics, traffic network, scientific collaboration, world wide web and social networks. Graph data mining is used to discover useful information and knowledge from graph data. The complications of nodes, links and the semi-structure form present challenges in terms of the computation tasks, e.g., node classification, link prediction, and graph classification. In this context, various advanced techniques, including graph embedding and graph neural networks, have recently been proposed to improve the performance of graph data mining. This book provides a state-of-the-art review of graph data mining methods. It addresses a current hot topic - the security of graph data mining - and proposes a series of detection methods to identify adversarial samples in graph data. In addition, it introduces readers to graph augmentation and subgraph networks to further enhance the models, i.e., improve their accuracy and robustness. Lastly, the book describes the applications of these advanced techniques in various scenarios, such as traffic networks, social and technical networks, and blockchains. ...

        Détails de conformité du produit

        Consulter les détails de conformité de ce produit (

        Personne responsable dans l'UE

        )
        Le choixNeuf et occasion
        Minimum5% remboursés
        La sécuritéSatisfait ou remboursé
        Le service clientsÀ votre écoute
        LinkedinFacebookTwitterInstagramYoutubePinterestTiktok
        visavisa
        mastercardmastercard
        klarnaklarna
        paypalpaypal
        floafloa
        americanexpressamericanexpress
        Rakuten Logo
        • Rakuten Kobo
        • Rakuten TV
        • Rakuten Viber
        • Rakuten Viki
        • Plus de services
        • À propos de Rakuten
        Rakuten.com