عنوان

Graph data mining :

ويژگيها

(electronic bk.)

ويژگيها

(electronic bk.)

شابک

9789811626098

شابک

981162609X

شابک اشتباه

9789811626081

شابک اشتباه

9811626081

شماره ISMN

10.1007/978-981-16-2609-8

شماره

15142

شماره کنترل سيستم

(OCoLC)1260401314

شماره کنترل سيستم غير معتبر يا حذف شده

(OCoLC)1261365932

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Graph data mining :

ساير اطلاعات عنواني

algorithm, security and application /

نام نخستين پديدآور

Qi Xuan, Zhongyuan Ruan, Yong Min, editors.

محل نشرو پخش و غیره

Singapore :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2021]

تاریخ نشرو بخش و غیره

�2021.

نام خاص و کميت اثر

243p.

ساير جزييات

illustrations (some color)

عنوان فروست

Big data management.

متن يادداشت

Available to OhioLINK libraries.

متن يادداشت

Includes bibliographical references.

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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.

شماره استاندارد بين المللي کتاب و موسيقي

9789811626081

شناسگر رکورد کتابشناختي

(OCoLC)1246352444.

عنصر شناسه ای

Data mining.

عنصر شناسه ای

Graph theory

تقسیم فرعی موضوعی

Data processing.

کد مقوله موضوعی

UNF

کد مقوله موضوعی

COM021030

کد مقوله موضوعی

UNF

کد مقوله موضوعی

UYQE

کد سيستم

bicssc

کد سيستم

bisacsh

کد سيستم

thema

کد سيستم

thema

شماره

ويراست

شماره رده

نشانه اثر

عنصر شناسه اي

Xuan, Qi,

عنصر شناسه اي

Ruan, Zhongyuan,

عنصر شناسه اي

Min, Yong,

عنصر شناسه اي

Ohio Library and Information Network.

سازمان

کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه

تاريخ عمليات

20231009071036.0

قواعد فهرست نويسي ( بخش توصيفي )

rda

تاريخ و ساعت مذاکره و دسترسي

Graph Data Mining(2021)[Xuan Ruan][9789811626098] (2021).pdf

مشخصه منبع قراردادي

https://rave.ohiolink.edu/ebooks/ebc2/9789811626098

مشخصه منبع قراردادي

https://link.springer.com/10.1007/978-981-16-2609-8

مشخصه منبع قراردادي

http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-981-16-2609-8

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource (off-campus)

نوع ماده

BL

کد کاربرگه

270410

تكميل شده

Y