Book Image

Building Websites with VB.NET and DotNetNuke 4

Book Image

Building Websites with VB.NET and DotNetNuke 4

Overview of this book

DotNetNuke is an open-source Content Management System and web application framework. DotNetNuke has taken the Microsoft world by storm and now at version 4, its community has grown to over 200,000 users. This book covers virtually everything you need to know to get your DotNetNuke website up and running. Concisely written and with clear explanations, this book is covers installation, administration, deployment, site creation and all of the basic built in DotNetNuke modules. For developers, chapters on the core architecture, skinning and custom modules, including coverage of the DAL+, give you the skills to customize and extend your site. The book starts off by giving you a deep understanding of working with basic DotNetNuke sites, guiding you through the features and giving you the confidence to create and manage your site. After that, you will journey to the heart of DotNetNuke, and learn about its core architecture. Always concise, relevant and practical, you will find out what makes DotNetNuke tick, and from there, you will be ready to customize DotNetNuke. Developers will enjoy the detailed walkthrough of creating a new custom modules. Special emphasis is given to the DAL+, an extended feature set of the DotNetNuke Data Access Layer (DAL). You will see how to create custom modules with the DAL+, and invigorate your module development. Web designers will enjoy the material on skinning, helping them to create a new look for their site. You will learn about creating new skins, and packaging them up for easy deployment. You will master all of this as you leap into the development of a DotNetNuke 4 site.

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Building Websites with VB.NET and DotNetNuke 4
Oct, 2006 | 0 hours 0 minutes
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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Free Chapter
1
Section 1 – Introduction to Graph Machine Learning
Section 1 – Introduction to Graph Machine Learning
2
Chapter 1: Getting Started with Graphs
Chapter 1: Getting Started with Graphs
Technical requirements
Introduction to graphs with networkx
Plotting graphs
Graph properties
Benchmarks and repositories
Dealing with large graphs
Summary
3
Chapter 2: Graph Machine Learning
Chapter 2: Graph Machine Learning
Technical requirements
Understanding machine learning on graphs
The generalized graph embedding problem
The taxonomy of graph embedding machine learning algorithms
Summary
4
Section 2 – Machine Learning on Graphs
Section 2 – Machine Learning on Graphs
5
Chapter 3: Unsupervised Graph Learning
Chapter 3: Unsupervised Graph Learning
Technical requirements
The unsupervised graph embedding roadmap
Shallow embedding methods
Autoencoders
Graph neural networks
Summary
6
Chapter 4: Supervised Graph Learning
Chapter 4: Supervised Graph Learning
Technical requirements
The supervised graph embedding roadmap
Feature-based methods
Shallow embedding methods
Graph regularization methods
Graph CNNs
Summary
7
Chapter 5: Problems with Machine Learning on Graphs
Chapter 5: Problems with Machine Learning on Graphs
Technical requirements
Predicting missing links in a graph
Detecting meaningful structures such as communities
Detecting graph similarities and graph matching
Summary
8
Section 3 – Advanced Applications of Graph Machine Learning
Section 3 – Advanced Applications of Graph Machine Learning
9
Chapter 6: Social Network Graphs
Chapter 6: Social Network Graphs
Technical requirements
Overview of the dataset
Network topology and community detection
Embedding for supervised and unsupervised tasks
Summary
10
Chapter 7: Text Analytics and Natural Language Processing Using Graphs
Chapter 7: Text Analytics and Natural Language Processing Using Graphs
Technical requirements
Providing a quick overview of a dataset
Understanding the main concepts and tools used in NLP
Creating graphs from a corpus of documents
Building a document topic classifier
Summary
11
Chapter 8:Graph Analysis for Credit Card Transactions
Chapter 8:Graph Analysis for Credit Card Transactions
Technical requirements
Overview of the dataset
Network topology and community detection
Embedding for supervised and unsupervised fraud detection
Summary
12
Chapter 9: Building a Data-Driven Graph-Powered Application
Chapter 9: Building a Data-Driven Graph-Powered Application
Technical requirements
Overview of Lambda architectures
Lambda architectures for graph-powered applications
Summary
13
Chapter 10: Novel Trends on Graphs
Chapter 10: Novel Trends on Graphs
Technical requirements
Learning about data augmentation for graphs
Learning about topological data analysis
Applying graph theory in new domains
Summary
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14
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Technical requirements

We will be using Jupyter Notebooks with Python 3.8 for all of our exercises. In the following code snippet, we show a list of Python libraries that will be installed for this chapter using pip (for example, run pip install networkx==2.5 on the command line, and so on):

Jupyter==1.0.0
networkx==2.5
snap-stanford==5.0.0
matplotlib==3.2.2
pandas==1.1.3
scipy==1.6.2

In this book, the following Python commands will be referred to:

  • import networkx as nx
  • import pandas as pd
  • import numpy as np

For more complex data visualization tasks, Gephi (https://gephi.org/) is also required. The installation manual is available here: https://gephi.org/users/install/. All code files relevant to this chapter are available at https://github.com/PacktPublishing/Graph-Machine-Learning/tree/main/Chapter01.

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