Modern Graph Theory Algorithms with Python Harness the Power of Graph Algorithms and Real-World Network Applications Using Python
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...
| Autor principal: | |
|---|---|
| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited
2024.
|
| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828036806719 |
Tabla de Contenidos:
- 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
- Technical requirements
- 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
- Summary
- References
- Part 2: Spatial Data Applications
- Chapter 3: Demographic Data
- Technical requirements
- Introduction to demography
- Demographic factors
- Geographic factors
- Homophily in networks
- Francophone Africa music spread
- AIMS Cameroon student network epidemic model
- Summary
- References
- Chapter 4: Transportation Data
- Technical requirements
- 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
- Summary
- References
- Chapter 5: Ecological Data
- Technical requirements
- 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
- Summary
- References
- Part 3: Temporal Data Applications.
- Chapter 6: Stock Market Data
- Technical requirements
- 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
- Summary
- References
- Chapter 7: Goods Prices/Sales Data
- Technical requirements
- An introduction to spatiotemporal data
- The Burkina Faso market dataset
- Store sales data
- Analyzing our spatiotemporal datasets
- Summary
- References
- Chapter 8: Dynamic Social Networks
- Technical requirements
- 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
- Summary
- References
- Part 4: Advanced Applications
- Chapter 9: Machine Learning for Networks
- Technical requirements
- 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
- Summary
- References
- Chapter 10: Pathway Mining
- Technical requirements
- 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
- Summary
- References
- Chapter 11: Mapping Language Families - an Ontological Approach
- Technical requirements
- What is an ontology?
- Introduction to ontologies
- Representing information as an ontology
- Language families
- Language drift and relationships
- Nilo-Saharan languages
- Mapping language families
- Summary
- References
- 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
- Summary
- References
- Chapter 13: Putting It All Together
- Technical requirements
- 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
- Summary
- References
- 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
- Summary
- References
- Index
- Other Books You May Enjoy.
