東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Graph theory- Data processing." ]

切換: 標籤 | MARC模式 | ISBD

Graph data mining = algorithm, secur...

Xuan, Qi.

FindBook

Google Book

Amazon

博客來

Graph data mining = algorithm, security and application /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Graph data mining/ edited by Qi Xuan, Zhongyuan Ruan, Yong Min.
其他題名:	algorithm, security and application /
其他作者:	Xuan, Qi.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xvi, 243 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 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.
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-981-16-2609-8
ISBN:	9789811626098

Graph data mining = algorithm, security and application /
Graph data miningalgorithm, security and application /[electronic resource] :edited by Qi Xuan, Zhongyuan Ruan, Yong Min. - Singapore :Springer Singapore :2021. - xvi, 243 p. :ill. (some col.), digital ;24 cm. - Big data management,2522-0179. - Big data management..

Chapter 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.

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.

ISBN: 9789811626098

Standard No.: 10.1007/978-981-16-2609-8doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / G73 2021

Dewey Class. No.: 006.312

Graph data mining = algorithm, security and application /
LDR:03219nmm a2200349 a 4500 001 2242058
003 DE-He213
005 20210715191829.0
006 m d
007 cr nn 008maaau
008 211207s2021 si s 0 eng d
020 $a 9789811626098 $q (electronic bk.)
020 $a 9789811626081 $q (paper)
024 7 $a 10.1007/978-981-16-2609-8 $2 doi
035 $a 978-981-16-2609-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b G73 2021
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b G766 2021
245 0 0 $a Graph data mining $h [electronic resource] : $b algorithm, security and application / $c edited by Qi Xuan, Zhongyuan Ruan, Yong Min.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xvi, 243 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Big data management, $x 2522-0179
505 0 $a Chapter 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 $a 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.
650 0 $a Data mining. $3 562972
650 0 $a Graph theory $x Data processing. $3 655277
650 1 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Privacy. $3 528582
700 1 $a Xuan, Qi. $3 3500824
700 1 $a Ruan, Zhongyuan. $3 3500825
700 1 $a Min, Yong. $3 3500826
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Big data management. $3 3500827
856 4 0 $u https://doi.org/10.1007/978-981-16-2609-8
950 $a Computer Science (SpringerNature-11645)