# Books for data science applications on graphs

Application of data science especially ML algos on complex structures that can be represented as graphs has risen tremendously. Research work in recent times in this area focus on topics like:-

Learning and mining algorithms:
Graph mining approaches
Learning to rank in networks
Similarity measures and graph kernel methods
Graph alignment, matching, and identification
Network summarization and compression
Learning from partially-observed networks
Semi-supervised learning, active learning, transductive inference, and transfer learning in the context of graphs
Large-scale analysis and models for graph data
Evaluation issues in graph-based algorithms
Anomaly detection with graph data
Embeddings and factorization methods:
Network embedding methods and manifold learning
Matrix and tensor factorization methods
Deep learning on graphs
Learning with dynamic and complex networks:
Models to learn from dynamic graph data
Heterogeneous, signed, attributed, and multi-relational graph mining methods
Online learning with graphs
Statistical and probabilistic methods:
Computational or statistical learning theory related to graphs
Statistical models of graph structures
Probabilistic and graphical models for structured
data
Statistical relational learning Sampling graph data


Are there good books that cover the exact above topics in good detail like graduate textbooks? I know there are excellent books that compile conference proceedings but I am looking for seminal books on these topics. This area is still very recent and young so it may be difficult to find a good book. One book that comes to mind is An Introduction to Statistical Learning: With Applications in R by Daniela Witten, Gareth James, Robert Tibshirani, and Trevor Hastie. But this book only focuses on ML and data science but not on its specific applications to graphs like the above topics.