Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets.
- Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time
- Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed
- Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate
- Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node
- Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups
Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.
Introduction. The core model. Background. Modelling relationships of different types. Modelling asymmetric relationships. Modelling asymmetric relationships with multiple types. Modelling relationships that change over time. Modelling positive and negative relationships. Signed graph-based semi-supervised learning. Combining directed and signed embeddings. Appendices