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Aug. 11, 2025, 9:34 a.m.
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(Last updated: Aug. 11, 2025, 9:35 a.m.)
| File | Size |
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| Bhattacharya R. Graph Mining. Practical Uses and Instruments...Networks 2026.pdf | 15.4 MB |
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| Uploaded by indexFroggy | Size 11.4 MB | Health [ 30 /0 ] | Added 2023-07-01 |
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55.5 MB
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| Uploaded by indexFroggy | Size 55.5 MB | Health [ 27 /5 ] | Added 2023-07-01 |
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SOURCE: Bhattacharya R. Graph Mining. Practical Uses and Instruments...Networks 2026
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COVER
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MEDIAINFO
Textbook in PDF format 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. Data in areas where translation is not an issue, such as images, text, and audio, are the exclusive purview of traditional convolutional neural networks. The ability to construct a convolutional neural network is made possible by translation invariance, which permits us to construct a globally shared convolution kernel in the input data space. By way of illustration, consider image data as a collection of uniformly distributed pixels in Euclidean space; because to translation invariance, each pixel can serve as the centre of a locally identical structure of the same size. This is the foundation upon which the CNN builds accurate concealed layer illustrations of images through the modelling of local links and the learning of convolution kernels shared at each pixel. The conventional CNNs perform better in the picture and text domains, but they are limited to working with data in the Cartesian coordinate system. Meanwhile, the pervasiveness of non-Euclidean spatial data, often known as graph data, has been attracting more and more attention. The networks in the real world, such transportation systems, the Internet, and social media, can be organically represented using graph data. Because graph data has a different local structure for each node than image and text data, translation invariance is no longer satisfied. One difficulty in defining CNN on graph data is the absence of translation invariance. 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). Beyond its inherent benefit in analyzing graph data, GNN's explainability and effective application across reasoning tasks make it a theoretically and practically accessible approach. While there are many different GNN models we focus mostly on graph convolutional networks (GCNs), since they beat a lot of graph Deep Learning models at different graph-based tasks. The most popular and influential approach now is Graph Convolutional Networks (GCN), which uses CNNs' capacity to simulate both local structures and the pervasive graph dependencies. While a few publications have surfaced recently to analyse and summarize Deep Learning on graphs, there is still a lack of thorough coverage and summaries of GCN's modelling methods and applications, the most relevant branch. A Comprehensive Overview of Graph Convolutional Network A Survey of Anomaly Detection in Graphs: Algorithms and Applications Analyzing Overlapping and Non-overlapping Communities in Complex Networks Efficient Cybersecurity Threat Analysis Through Anomaly Detection and Graph Summarization Efficient Frequent Subgraph Mining: Algorithms and Applications in Complex Networks... Link Prediction in Graph-Based Data: Techniques for Analyzing and Predicting Network Connections Unveiling Power Laws in Graph Mining: Techniques and Applications in Graph Query Analysis A Graph Neural Network Approach to Personalized Movie Recommendations Through Link Prediction in Graph-Based Data Citation Knowledge Graphs for Academic Insights: Modelling, Processing, and Analysis Integrating Graph Convolutional Networks for Web Traffic Prediction
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