English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 9h 45m | 1.51 GB
Practical methods for analyzing your data with graphs, revealing hidden connections and new insights.
Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don’t need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.
In Graph Algorithms for Data Science you will learn:
- Labeled-property graph modeling
- Constructing a graph from structured data such as CSV or SQL
- NLP techniques to construct a graph from unstructured data
- Cypher query language syntax to manipulate data and extract insights
- Social network analysis algorithms like PageRank and community detection
- How to translate graph structure to a ML model input with node embedding models
- Using graph features in node classification and link prediction workflows
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It’s filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You’ll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.
A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more.
Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding.
What’s inside
- Creating knowledge graphs
- Node classification and link prediction workflows
- NLP techniques for graph construction
Table of Contents
1 Part 1. Introduction to graphs
2 Graphs and network science An introduction
3 How to spot a graph-shaped problem
4 Summary
5 Representing network structure Designing your first graph model
6 Network representations
7 Designing your first labeled-property graph model
8 Extracting knowledge from text
9 Summary
10 Part 2. Network analysis
11 Your first steps with Cypher query language
12 Importing CSV files with Cypher
13 Solutions to exercises
14 Summary
15 Exploratory graph analysis
16 Aggregating data with Cypher query language
17 Filtering graph patterns
18 Counting subqueries
19 Multiple aggregations in sequence
20 Solutions to exercises
21 Summary
22 Introduction to social network analysis
23 Introduction to the Neo4j Graph Data Science library
24 Network characterization
25 Identifying central nodes
26 Solutions to exercises
27 Summary
28 Projecting monopartite networks
29 Retweet network characterization
30 Identifying the most influential content creators
31 Solutions to exercises
32 Summary
33 Inferring co-occurrence networks based on bipartite networks
34 Constructing the co-occurrence network
35 Characterization of the co-occurrence network
36 Community detection with the label propagation algorithm
37 Identifying community representatives with PageRank
38 Solutions to exercises
39 Summary
40 Constructing a nearest neighbor similarity network
41 Constructing the nearest neighbor graph
42 User segmentation with the community detection algorithm
43 Solutions to exercises
44 Summary
45 Part 3. Graph machine learning
46 Node embeddings and classification
47 Node classification task
48 The node2vec algorithm
49 Solutions to exercises
50 Summary
51 Link prediction
52 Dataset split
53 Network feature engineering
54 Link prediction classification model
55 Solutions to exercises
56 Summary
57 Knowledge graph completion
58 Knowledge graph completion
59 Solutions to exercises
60 Summary
61 Constructing a graph using natural language processing techniques
62 Named entity recognition
63 Relation extraction
64 Implementation of information extraction pipeline
65 Solutions to exercises
66 Summary
67 The Neo4j environment
68 Neo4j installation
69 Neo4j Browser configuration
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