Graph Data Mining : Algorithm, Security and Application /

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 discove...

Full description

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Xuan, Qi (Editor), Ruan, Zhongyuan (Editor), Min, Yong (Editor)
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Series:	Big Data Management,
Subjects:	Data mining. Machine learning. Data structures (Computer science). Artificial intelligence. Computer security. Data Mining and Knowledge Discovery. Machine Learning. Data Structures and Information Theory. Artificial Intelligence. Privacy. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.