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001 | 978-981-16-2609-8 | ||
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_a9789811626098 _9978-981-16-2609-8 |
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024 | 7 |
_a10.1007/978-981-16-2609-8 _2doi |
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050 | 4 | _aQA76.9.D343 | |
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_a006.312 _223 |
245 | 1 | 0 |
_aGraph Data Mining _h[electronic resource] : _bAlgorithm, Security and Application / _cedited by Qi Xuan, Zhongyuan Ruan, Yong Min. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021. |
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300 |
_aXVI, 243 p. 92 illus., 67 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aBig Data Management, _x2522-0187 |
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505 | 0 | _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. | |
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. . | ||
650 | 0 | _aData mining. | |
650 | 0 | _aMachine learning. | |
650 | 0 |
_aArtificial intelligence _xData processing. |
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650 | 0 |
_aData protection _xLaw and legislation. |
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650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aData Science. |
650 | 2 | 4 | _aPrivacy. |
700 | 1 |
_aXuan, Qi. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aRuan, Zhongyuan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aMin, Yong. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811626081 |
776 | 0 | 8 |
_iPrinted edition: _z9789811626104 |
776 | 0 | 8 |
_iPrinted edition: _z9789811626111 |
830 | 0 |
_aBig Data Management, _x2522-0187 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-2609-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c178564 _d178564 |