{"product_id":"statistical-and-machine-learning-approaches-for-network-analysis-hardback-9780470195154","title":"Statistical and Machine Learning Approaches for Network Analysis (Hardback) 9780470195154","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistical and Machine Learning Approaches for Network Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eMatthias Dehmer (Author), Subhash C. Basak (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470195154, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 7 September 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages, Graphs: 50 B\u0026amp;W, 0 Color\u003cbr\u003e24.1 x 16.3 x 2.3 cm, 0.608 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eExplore the multidisciplinary nature of complex networks through machine learning techniques\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eStatistical and Machine Learning Approaches for Network Analysis\u003c\/i\u003e provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks.\u003c\/p\u003e \u003cp\u003eComprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA survey of computational approaches to reconstruct and partition biological networks\u003c\/li\u003e \u003cli\u003eAn introduction to complex networks—measures, statistical properties, and models\u003c\/li\u003e \u003cli\u003eModeling for evolving biological networks\u003c\/li\u003e \u003cli\u003eThe structure of an evolving random bipartite graph\u003c\/li\u003e \u003cli\u003eDensity-based enumeration in structured data\u003c\/li\u003e \u003cli\u003eHyponym extraction employing a weighted graph kernel\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eStatistical and Machine Learning Approaches for Network Analysis\u003c\/i\u003e is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eContributors xi\u003c\/p\u003e \u003cp\u003e1 A Survey of Computational Approaches to Reconstruct and Partition Biological Networks 1\u003cbr\u003e\u003ci\u003eLipi Acharya, Thair Judeh, and Dongxiao Zhu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Introduction to Complex Networks: Measures, Statistical Properties, and Models 45\u003cbr\u003e\u003ci\u003eKazuhiro Takemoto and Chikoo Oosawa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Modeling for Evolving Biological Networks 77\u003cbr\u003e\u003ci\u003eKazuhiro Takemoto and Chikoo Oosawa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Modularity Configurations in Biological Networks with Embedded Dynamics 109\u003cbr\u003e\u003ci\u003eEnrico Capobianco, Antonella Travaglione, and Elisabetta Marras\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Influence of Statistical Estimators on the Large-Scale Causal Inference of Regulatory Networks 131\u003cbr\u003e\u003ci\u003eRicardo de Matos Simoes and Frank Emmert-Streib\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Weighted Spectral Distribution: A Metric for Structural Analysis of Networks 153\u003cbr\u003e\u003ci\u003eDamien Fay, Hamed Haddadi, Andrew W. Moore, Richard Mortier, Andrew G. Thomason, and Steve Uhlig\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 The Structure of an Evolving Random Bipartite Graph 191\u003cbr\u003e\u003ci\u003eReinhard Kutzelnigg\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Graph Kernels 217\u003cbr\u003e\u003ci\u003eMatthias Rupp\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Network-Based Information Synergy Analysis for Alzheimer Disease 245\u003cbr\u003e\u003ci\u003eXuewei Wang, Hirosha Geekiyanage, and Christina Chan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Density-Based Set Enumeration in Structured Data 261\u003cbr\u003e\u003ci\u003eElisabeth Georgii and Koji Tsuda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Hyponym Extraction Employing a Weighted Graph Kernel 303\u003cbr\u003e\u003ci\u003eTim vor der Br¨uck\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52257179173144,"sku":"9780470195154","price":97.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470195154.jpg?v=1781277997","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistical-and-machine-learning-approaches-for-network-analysis-hardback-9780470195154","provider":"Freshly Printed Books","version":"1.0","type":"link"}