Graph Algorithms for Data Science Second Edition Version 4

Graph Algorithms for Data Science MEAP V04
Copyright
welcome
brief contents
Chapter 1: Graphs and network science: An introduction
	1.1 Introduction to graph theory
		1.1.1 What is a graph?
	1.2 How to spot a graph-shaped problem
	1.3 Machine learning on graphs
	1.4 Summary
	1.5 References
Chapter 2: Representing network structure - design your first graph model
	2.1 Graph databases
		2.1.1 RDF Graph Database
		2.1.2 Labeled-property graph database
	2.2 Designing your first labeled-property graph model
		2.2.1 Follower network
		2.2.2 User - Tweet network
		2.2.3 Retweet network
		2.2.4 Representing graph schema
	2.3 Extracting knowledge from text
		2.3.1 Links
		2.3.2 Hashtags
		2.3.3 Mentions
		2.3.4 Final Twitter social network schema
	2.4 Summary
Chapter 3: Your first steps with the Cypher query language
	3.1 Cypher query language clauses
		3.1.1 RETURN clause
		3.1.2 WITH clause
		3.1.3 CREATE clause
		3.1.4 MATCH clause
		3.1.5 Set clause
		3.1.6 REMOVE clause
		3.1.7 DELETE clause
		3.1.8 MERGE clause
	3.2 Importing CSV files with Cypher
		3.2.1 Cleanup the database
		3.2.2 Twitter graph model
		3.2.3 Unique constraints
		3.2.4 LOAD CSV clause
		3.2.5 Importing the Twitter social network
	3.3 Summary
Chapter 4: Cypher aggregations and social network analysis
	4.1 Exploring the Twitter network with Cypher query language
		4.1.1 Aggregating data with Cypher query language
		4.1.2 Time aggregations
		4.1.3 Filtering graph patterns
		4.1.4 Counting relationships in Neo4j
	4.2 Introduction to social network analysis
		4.2.1 Node degree distribution
		4.2.2 Neo4j Graph Data Science library
		4.2.3 Graph Catalog and Native projection
		4.2.4 Weakly Connected Component algorithm
		4.2.5 Strongly Connected Components algorithm
		4.2.6 Local clustering coefficient
		4.2.7 Finding influencers with the PageRank algorithm
		4.2.8 Drop named graph
	4.3 Summary
	4.4 References
Chapter 5: Introduction to social network analysis
	5.1 Followers network analysis
		5.1.1 Node degree distribution
		5.1.2 Introduction to Neo4j Graph Data Science library
		5.1.3 Graph Catalog and Native projection
		5.1.4 Weakly Connected Component algorithm
		5.1.5 Strongly Connected Components algorithm
		5.1.6 Local clustering coefficient
		5.1.7 Finding influencers with the PageRank algorithm
		5.1.8 Drop named graph
	5.2 Summary
	5.3 References
Appendix A: Adjacency matrix