Description
Book Synopsis* Provides a general framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for graph classification. * The proposed methods are applied to different real data sets to demonstrate their ability.
Table of ContentsPreface ix
Contributors xi
1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks 1
Lipi Acharya, Thair Judeh, and Dongxiao Zhu
2 Introduction to Complex Networks: Measures, Statistical Properties, and Models 45
Kazuhiro Takemoto and Chikoo Oosawa
3 Modeling for Evolving Biological Networks 77
Kazuhiro Takemoto and Chikoo Oosawa
4 Modularity Configurations in Biological Networks with Embedded Dynamics 109
Enrico Capobianco, Antonella Travaglione, and Elisabetta Marras
5 Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks 131
Ricardo de Matos Simoes and Frank Emmert-Streib
6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks 153
Damien Fay, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason, and Steve Uhlig
7 The Structure of an Evolving Random Bipartite Graph 191
Reinhard Kutzelnigg
8 Graph Kernels 217
Matthias Rupp
9 Network-Based Information Synergy Analysis for Alzheimer Disease 245
Xuewei Wang, Hirosha Geekiyanage, and Christina Chan
10 Density-Based Set Enumeration in Structured Data 261
Elisabeth Georgii and Koji Tsuda
11 Hyponym Extraction Employing a Weighted Graph Kernel 303
Tim vor der Br¨uck
Index 327
