Graph Algorithms for Data Science Second Edition Version 6

✍ Scribed by Tomaž Bratanič

• Publisher: The MathWorks, Inc.
• Year: 2022
• Tongue: English
• Leaves: 241
• Edition: MEAP Edition
• Category: Library

✦ Table of Contents

Graph Algorithms for Data Science MEAP V06
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: Exploratory graph 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 Summary
4.3 References
4.4 Solutions to exercises
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
Chapter 6: Projecting monopartite networks with Cypher Projection
6.1 Translate an indirect multi-hop path into a direct relationship
6.1.1 Cypher Projection
6.1.2 Degree centrality
6.1.3 Weakly Connected Components
6.1.4 Weighted PageRank
6.1.5 Drop projected in-memory graph
6.2 Summary
6.3 References
6.4 Solutions to exercises
Chapter 7: Inferring co-occurrence networks based off bipartite networks
7.1 Extracting hashtags from tweets
7.2 Analyzing the co-occurrence network
7.2.1 Node Similarity algorithm
7.2.2 Co-occurence network characterization
7.2.3 Inspect community structure with Label Propagation algorithm
7.2.4 Drop projected in-memory graphs
7.3 Summary
7.4 References
7.5 Solutions to exercises
Chapter 8: Constructing a nearest neighbor similarity network
8.1 Feature extraction
8.1.1 Motifs & Graphlets
8.1.2 Betweenness centrality
8.1.3 Closeness centrality
8.2 Constructing the nearest neighbor graph
8.2.1 Evaluate features
8.2.2 Inferring the similarity network
8.3 User segmentation with community detection algorithm
8.4 Summary
8.5 References
8.6 Solutions to exercises
Chapter 9: Node embeddings and classification
9.1 Node embedding models
9.1.1 Homophily versus structural roles approach
9.1.2 Inductive versus transductive embedding models
9.2 Node classification task
9.2.1 Define a connection to Neo4j database
9.2.2 Import twitch dataset
9.3 Node2vec algorithm
9.3.1 Word2vec algorithm
9.3.2 Random walks
9.3.3 Calculate node2vec embeddings
9.3.4 Evaluate node embeddings
9.3.5 Train a classification model
9.3.6 Evaluate predictions
9.4 Summary
9.5 References
9.6 Solutions to exercises