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Download Graph Mining: Practical Uses by Riju Bhattacharya in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About Graph Mining: Practical Uses
This book provides a thorough introduction to graph mining and addresses foundational concepts and advanced techniques along with practical applications across various fields. As graphs have become increasingly vital for data representation in domains such as social network analysis, bioinformatics, and transportation, there is a growing demand for a comprehensive source that covers both theory and practical insights. This book seeks to fill that gap by offering clear explanations, practical examples, and actionable insights, making complex graph mining techniques accessible to students, postgraduate readers, and researchers. The authors also provide an extensive investigation into the process of gaining insightful knowledge from graph representations, ranging from interpreting intricate relationships to decoding complex data structures. Readers will learn to identify anomalous patterns, locate communities, arrange nodes, predict connections, and evaluate graphs effectively. A convolutional neural network (CNN) is a outstanding modelling abilities have contributed to its exponential rise in popularity in recent years. The fields of image processing and Natural Language Processing (NLP), including machine translation, image recognition, and speech recognition, among others, have made great strides since the advent of CNN compared to earlier methods. Researchers have started to concentrate on ways to build Deep Learning models on graphs in recent years, probably because graph data is so ubiquitous. A prominent model is the graph neural network (GNN).
Detailed Information
| Author: | Riju Bhattacharya |
|---|---|
| Pages: | 137 |
| Language: | English |
| File Size: | 6.6 |
| Format: | |
| Price: | FREE |
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