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
   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.


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