001438265 000__ 04191cam\a2200589\i\4500
001438265 001__ 1438265
001438265 003__ OCoLC
001438265 005__ 20230309004256.0
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001438265 007__ cr\un\nnnunnun
001438265 008__ 210718s2021\\\\si\a\\\\ob\\\\000\0\eng\d
001438265 019__ $$a1261365932$$a1266810108
001438265 020__ $$a9789811626098$$q(electronic bk.)
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001438265 020__ $$z9789811626081
001438265 020__ $$z9811626081
001438265 0247_ $$a10.1007/978-981-16-2609-8$$2doi
001438265 035__ $$aSP(OCoLC)1260401314
001438265 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dEBLCP$$dGW5XE$$dOCLCO$$dDCT$$dOCLCF$$dUKAHL$$dOCLCO$$dOCLCQ$$dN$T$$dOCLCQ
001438265 049__ $$aISEA
001438265 050_4 $$aQA76.9.D343$$bG73 2021
001438265 08204 $$a006.3/12$$223
001438265 24500 $$aGraph data mining :$$balgorithm, security and application /$$cQi Xuan, Zhongyuan Ruan, Yong Min, editors.
001438265 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001438265 264_4 $$c©2021
001438265 300__ $$a1 online resource :$$billustrations (some color)
001438265 336__ $$atext$$btxt$$2rdacontent
001438265 337__ $$acomputer$$bc$$2rdamedia
001438265 338__ $$aonline resource$$bcr$$2rdacarrier
001438265 347__ $$atext file
001438265 347__ $$bPDF
001438265 4901_ $$aBig data management
001438265 504__ $$aIncludes bibliographical references.
001438265 5050_ $$aChapter 1. Information Source Estimation with Multi-Channel Graph Neural Network -- Chapter 2. Link Prediction based on Hyper-Substructure Network -- Chapter 3. Broad Learning Based on Subgraph Networks for Graph Classification -- Chapter 4. Subgraph Augmentation with Application to Graph Mining -- 5. Adversarial Attacks on Graphs: How to Hide Your Structural Information -- Chapter 6. Adversarial Defenses on Graphs: Towards Increasing the Robustness of Algorithms -- Chapter 7. Understanding Ethereum Transactions via Network Approach -- Chapter 8. Find Your Meal Pal: A Case Study on Yelp Network -- Chapter 9. Graph convolutional recurrent neural networks: a deep learning framework for traffic prediction -- Chapter 10. Time Series Classification based on Complex Network -- Chapter 11. Exploring the Controlled Experiment by Social Bots.
001438265 506__ $$aAccess limited to authorized users.
001438265 520__ $$aGraph 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.
001438265 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 2, 2021).
001438265 650_0 $$aData mining.
001438265 650_0 $$aGraph theory$$xData processing.
001438265 650_6 $$aExploration de données (Informatique)
001438265 655_0 $$aElectronic books.
001438265 7001_ $$aXuan, Qi,$$eeditor.
001438265 7001_ $$aRuan, Zhongyuan,$$eeditor.
001438265 7001_ $$aMin, Yong,$$eeditor.
001438265 77608 $$iPrint version:$$tGraph data mining.$$dSingapore : Springer, [2021]$$z9811626081$$z9789811626081$$w(OCoLC)1246352444
001438265 830_0 $$aBig data management.
001438265 852__ $$bebk
001438265 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2609-8$$zOnline Access$$91397441.1
001438265 909CO $$ooai:library.usi.edu:1438265$$pGLOBAL_SET
001438265 980__ $$aBIB
001438265 980__ $$aEBOOK
001438265 982__ $$aEbook
001438265 983__ $$aOnline
001438265 994__ $$a92$$bISE