Graph Algorithms for Data Science: With examples in Neo4j

inside front cover
Graph Algorithms for Data Science
Copyright
contents
front matter
   foreword
   preface
   acknowledgments
   about this book
      Who should read this book
      How this book is organized
      About the code
      liveBook discussion forum
   about the author
   about the cover illustration
Part 1 Introduction to graphs
1 Graphs and network science: An introduction
   1.1 Understanding data through relationships
   1.2 How to spot a graph-shaped problem
      1.2.1 Self-referencing relationships
      1.2.2 Pathfinding networks
      1.2.3 Bipartite graphs
      1.2.4 Complex networks
   Summary
2 Representing network structure: Designing your first graph model
   2.1 Graph terminology
      2.1.1 Directed vs. undirected graph
      2.1.2 Weighted vs. unweighted graphs
      2.1.3 Bipartite vs. monopartite graphs
      2.1.4 Multigraph vs. simple graph
      2.1.5 A complete graph
   2.2 Network representations
      2.2.1 Labeled-property graph model
   2.3 Designing your first labeled-property graph model
      2.3.1 Follower network
      2.3.2 User-tweet network
      2.3.3 Retweet network
      2.3.4 Representing graph schema
   2.4 Extracting knowledge from text
      2.4.1 Links
      2.4.2 Hashtags
      2.4.3 Mentions
      2.4.4 Final Twitter social network schema
   Summary
Part 2 Network analysis
3 Your first steps with Cypher query language
   3.1 Cypher query language clauses
      3.1.1 CREATE clause
      3.1.2 MATCH clause
      3.1.3 WITH clause
      3.1.4 SET clause
      3.1.5 REMOVE clause
      3.1.6 DELETE clause
      3.1.7 MERGE clause
   3.2 Importing CSV files with Cypher
      3.2.1 Clean up 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 Solutions to exercises
   Summary
4 Exploratory graph analysis
   4.1 Exploring the Twitter network
   4.2 Aggregating data with Cypher query language
      4.2.1 Time aggregations
   4.3 Filtering graph patterns
   4.4 Counting subqueries
   4.5 Multiple aggregations in sequence
   4.6 Solutions to exercises
   Summary
5 Introduction to social network analysis
   5.1 Follower network
      5.1.1 Node degree distribution
   5.2 Introduction to the Neo4j Graph Data Science library
      5.2.1 Graph catalog and native projection
   5.3 Network characterization
      5.3.1 Weakly connected component algorithm
      5.3.2 Strongly connected components algorithm
      5.3.3 Local clustering coefficient
   5.4 Identifying central nodes
      5.4.1 PageRank algorithm
      5.4.2 Personalized PageRank algorithm
      5.4.3 Dropping the named graph
   5.5 Solutions to exercises
   Summary
6 Projecting monopartite networks
   6.1 Translating an indirect multihop path into a direct relationship
      6.1.1 Cypher projection
   6.2 Retweet network characterization
      6.2.1 Degree centrality
      6.2.2 Weakly connected components
   6.3 Identifying the most influential content creators
      6.3.1 Excluding self-loops
      6.3.2 Weighted PageRank variant
      6.3.3 Dropping the projected in-memory graph
   6.4 Solutions to exercises
   Summary
7 Inferring co-occurrence networks based on bipartite networks
   7.1 Extracting hashtags from tweets
   7.2 Constructing the co-occurrence network
      7.2.1 Jaccard similarity coefficient
      7.2.2 Node similarity algorithm
   7.3 Characterization of the co-occurrence network
      7.3.1 Node degree centrality
      7.3.2 Weakly connected components
   7.4 Community detection with the label propagation algorithm
   7.5 Identifying community representatives with PageRank
      7.5.1 Dropping the projected in-memory graphs
   7.6 Solutions to exercises
   Summary
8 Constructing a nearest neighbor similarity network
   8.1 Feature extraction
      8.1.1 Motifs and graphlets
      8.1.2 Betweenness centrality
      8.1.3 Closeness centrality
   8.2 Constructing the nearest neighbor graph
      8.2.1 Evaluating features
      8.2.2 Inferring the similarity network
   8.3 User segmentation with the community detection algorithm
   8.4 Solutions to exercises
   Summary
Part 3 Graph machine learning
9 Node embeddings and classification
   9.1 Node embedding models
      9.1.1 Homophily vs. structural roles approach
      9.1.2 Inductive vs. transductive embedding models
   9.2 Node classification task
      9.2.1 Defining a connection to a Neo4j database
      9.2.2 Importing a Twitch dataset
   9.3 The node2vec algorithm
      9.3.1 The word2vec algorithm
      9.3.2 Random walks
      9.3.3 Calculate node2vec embeddings
      9.3.4 Evaluating node embeddings
      9.3.5 Training a classification model
      9.3.6 Evaluating predictions
   9.4 Solutions to exercises
   Summary
10 Link prediction
   10.1 Link prediction workflow
   10.2 Dataset split
   10.2.1 Time-based split
   10.2.2 Random split
   10.2.3 Negative samples
   10.3 Network feature engineering
   10.3.1 Network distance
   10.3.2 Preferential attachment
   10.3.3 Common neighbors
   10.3.4 Adamic-Adar index
   10.3.5 Clustering coefficient of common neighbors
   10.4 Link prediction classification model
   10.4.1 Missing values
   10.4.2 Training the model
   10.4.3 Evaluating the model
   10.5 Solutions to exercises
   Summary
11 Knowledge graph completion
   11.1 Knowledge graph embedding model
      11.1.1 Triple
      11.1.2 TransE
      11.1.3 TransE limitations
   11.2 Knowledge graph completion
      11.2.1 Hetionet
      11.2.2 Dataset split
      11.2.3 Train a PairRE model
      11.2.4 Drug application predictions
      11.2.5 Explaining predictions
   11.3 Solutions to exercises
   Summary
12 Constructing a graph using natural language processing techniques
   12.1 Coreference resolution
   12.2 Named entity recognition
      12.2.1 Entity linking
   12.3 Relation extraction
   12.4 Implementation of information extraction pipeline
      12.4.1 SpaCy
      12.4.2 Corefence resolution
      12.4.3 End-to-end relation extraction
      12.4.4 Entity linking
      12.4.5 External data enrichment
   12.5 Solutions to exercises
   Summary
Appendix. The Neo4j environment
   A.1 Cypher query language
   A.2 Neo4j installation
      A.2.1 Neo4j Desktop installation
      A.2.2 Neo4j Docker installation
      A.2.3 Neo4j Aura
   A.3 Neo4j Browser configuration
references
index