Author Description

This Book Explores Novel Graph Classes And Presents Novel Methods To Classify Networks. It Particularly Addresses The Following Problems: Exploration Of Novel Graph Classes And Their Relationships Among Each Other; Existing And Classical Methods To Analyze Networks; Novel Graph Similarity And Graph Classification Techniques Based On Machine Learning Methods; And Applications Of Graph Classification And Graph Mining. Key Topics Are Addressed In Depth Including The Mathematical Definition Of Novel Graph Classes, I.e. Generalized Trees And Directed Universal Hierarchical Graphs, And The Application Areas In Which To Apply Graph Classes To Practical Problems In Computational Biology, Computer Science, Mathematics, Mathematical Psychology, Etc-- Statistical And Machine Learning Approaches For Network Analysis; Contents; Preface; Contributors; 1 A Survey Of Computational Approaches To Reconstruct And Partition Biological Networks; 1.1 Introduction; 1.2 Biological Networks; 1.2.1 Directed Networks; 1.2.2 Undirected Networks; 1.3 Genome-wide Measurements; 1.3.1 Gene Expression Data; 1.3.2 Gene Sets; 1.4 Reconstruction Of Biological Networks; 1.4.1 Reconstruction Of Directed Networks; 1.4.1.1 Boolean Networks; 1.4.1.2 Probabilistic Boolean Networks; 1.4.1.3 Bayesian Networks; 1.4.1.4 Collaborative Graph Model; 1.4.1.5 Frequency Method 1.4.1.6 Em-based Inference From Gene Sets1.4.2 Reconstruction Of Undirected Networks; 1.4.2.1 Relevance Networks; 1.4.2.2 Graphical Gaussian Models; 1.5 Partitioning Biological Networks; 1.5.1 Directed And Undirected Networks; 1.5.2 Partitioning Undirected Networks; 1.5.2.1 Kernighan-lin Algorithm; 1.5.2.2 Girvan-newman Algorithm; 1.5.2.3 Newman's Eigenvector Method; 1.5.2.4 Infomap; 1.5.2.5 Clique Percolation Method; 1.5.3 Partitioning Directed Networks; 1.5.3.1 Newman's Eigenvector Method; 1.5.3.2 Infomap; 1.5.3.3 Clique Percolation Method; 1.6 Discussion; References 2 Introduction To Complex Networks: Measures, Statistical Properties, And Mo