Part 4: Advanced Applications

Part 4 introduces more advanced algorithms to wrangle network problems, including graph neural networks, vertex clustering, Bayesian networks, ontology web language, subgraphs mining, and graph databases. The problems tackled in this part include the clustering of social network vertices by demographic and network structure factors, understanding the evolution of substance misuse, mining causal pathways related to student learning outcomes, creating gene ontologies, comparing the language classifications of Nilo-Saharan languages, mapping food webs, and creating movie databases with a variety of relationships.

Of note in Part 4 are Chapters 13 and 14. Chapter 13 combines concepts from throughout this book to construct and analyze data related to Ebola outbreaks in the Democratic Republic of Congo. Chapter 14 presents cutting-edge network science applications, including quantum network science, network analysis of deep learning architectures, higher-order structuring of networks, and hypergraphs with a focus on medical, environmental, image, and language data applications.

This part has the following chapters:

• Chapter 9, Machine Learning for Networks
• Chapter 10, Pathway Mining
• Chapter 11, Mapping Language Families – an Ontological Approach
• Chapter 12, Graph Databases
• Chapter 13, Putting It All Together
• Chapter 14, New Frontiers