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 Link and relationship strength prediction 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.