Details

Subject(s)

• Python (Computer program language) [Browse]
• Computer algorithms [Browse]

Related name

• Mutombo, Franck Kalala [Browse]
• Giske, Michael [Browse]

Summary note

We are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.

Notes

• Description based upon print version of record.
• Friendship network introduction

Source of description

Description based on publisher supplied metadata and other sources.

Contents

• Cover
• Title Page
• Copyright and Credits
• Dedications
• Foreword
• Contributors
• Table of Contents
• Preface
• Part 1: Introduction to Graphs and Networks with Examples
• Chapter 1: What is a Network?
• Technical requirements
• Introduction to graph theory and networks
• Formal definitions
• Creating networks in Python
• Random graphs
• Examples of real-world social networks
• Other type of networks
• Advanced use cases of network science
• Summary
• References
• Chapter 2: Wrangling Data into Networks with NetworkX and igraph
• Introduction to different data sources
• Social interaction data
• Spatial data
• Temporal data
• Biological networks
• Other types of data
• Wrangling data into networks with igraph
• Social network examples with NetworkX
• Part 2: Spatial Data Applications
• Chapter 3: Demographic Data
• Introduction to demography
• Demographic factors
• Geographic factors
• Homophily in networks
• Francophone Africa music spread
• AIMS Cameroon student network epidemic model
• Chapter 4: Transportation Data
• Introduction to transportation problems
• Paths between stores
• Fuel costs
• Time to deliver goods
• Navigational hazards
• Shortest path applications
• Traveling salesman problem
• Max-flow min-cut algorithm
• Chapter 5: Ecological Data
• Introduction to ecological data
• Exploring methods to track animal populations across geographies
• Exploring methods to capture plant distributions and diseases
• Spectral graph tools
• Clustering ecological populations using spectral graph tools
• Spectral clustering on text notes
• Part 3: Temporal Data Applications.
• Chapter 6: Stock Market Data
• Introduction to temporal data
• Stock market applications
• Introduction to centrality metrics
• Application of centrality metrics across time slices
• Extending network metrics for time series analytics
• Chapter 7: Goods Prices/Sales Data
• An introduction to spatiotemporal data
• The Burkina Faso market dataset
• Store sales data
• Analyzing our spatiotemporal datasets
• Chapter 8: Dynamic Social Networks
• Social networks that change over time
• Friendship networks
• Triadic closure
• A deeper dive into spreading on networks
• Dynamic network introduction
• SIR models, Part Two
• Factors influencing spread
• Example with evolving wildlife interaction datasets
• Crocodile network
• Heron network
• Part 4: Advanced Applications
• Chapter 9: Machine Learning for Networks
• Introduction to friendship networks and friendship relational datasets
• Friendship network introduction
• Friendship demographic and school factor dataset
• ML on networks
• Clustering based on student factors
• Clustering based on student factors and network metrics
• Spectral clustering on the friendship network
• DL on networks
• GNN introduction
• Example GNN classifying the Karate Network dataset
• Chapter 10: Pathway Mining
• Introduction to Bayesian networks and causal pathways
• Bayes' Theorem
• Causal pathways
• Bayesian networks
• Educational pathway example
• Outcomes in education
• Course sequences
• Antecedents to success
• Analyzing course sequencing to find optimal student pathways to graduation
• Introduction to a dataset
• bnlearn analysis.
• Structural equation models
• Chapter 11: Mapping Language Families - an Ontological Approach
• What is an ontology?
• Introduction to ontologies
• Representing information as an ontology
• Language families
• Language drift and relationships
• Nilo-Saharan languages
• Mapping language families
• Chapter 12: Graph Databases
• Introduction to graph databases
• What is a graph database?
• What can you represent in a graph database?
• Querying and modifying data in Neo4j
• Basic query example
• More complicated query examples
• Chapter 13: Putting It All Together
• Introduction to the problem
• Ebola spread in the Democratic Republic of Congo - 2018-2020 outbreak
• Geography and logistics
• Introduction to GEEs
• Mathematics of GEEs
• Our problem and GEE formulation
• Data transformation
• Python wrangling
• GEE input
• Data modeling
• Running the GEE in Python
• Chapter 14: New Frontiers
• Quantum network science algorithms
• Graph coloring algorithms
• Max flow/min cut
• Neural network architectures as graphs
• Deep learning layers and connections
• Analyzing architectures
• Hierarchical networks
• Higher-order structures and network data
• An example using gene families
• Hypergraphs
• Displaying information
• Metadata
• Index
• Other Books You May Enjoy.

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ISBN

1-80512-017-4

OCLC

1436832843

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