Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics (SpringerBriefs in Complexity)
$58.51
Description
Book Synopsis: This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties. After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem of network reconstruction and introduces various advanced techniques to reliably infer the topology of a network from partial local information. Chapter 5 is devoted to the reformulation of certain “hard” combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a “softened” maximum-entropy framework. A final chapter offers various overarching remarks and take-home messages. By requiring no prior knowledge of network theory, the book targets a broad audience ranging from PhD students approaching these topics for the first time to senior researchers interested in the application of advanced network techniques to their field.
Details
Want to uncover the hidden patterns in complex networks? Look no further than Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics. This groundbreaking book revolutionizes the study of real networks and graph theory by introducing maximum-entropy models of random graphs. With its unified framework, it tackles various problems in network analysis and graph theory, providing solutions that were previously thought to be separate.
One of the key strengths of this book is its focus on pattern detection. By employing advanced techniques, it allows you to disentangle intricate higher-order structural features in real networks. You can go beyond simple local constraints and uncover the true complexity of network connections. Whether you are a PhD student or a senior researcher, this book offers valuable insights into the nuances behind network patterns.
Are you faced with the challenge of reconstructing a network from partial information? Maximum-Entropy Networks provides you with an array of reliable techniques to infer the topology of any network. No longer will you find yourself struggling to complete the puzzle of network reconstruction. With the help of this book, you will gain the expertise needed to confidently reconstruct networks, even with limited data.
Are you tired of combinatorial operations that drain your time and resources? Maximum-Entropy Networks paves the way for a simplified approach by introducing a "softened" maximum-entropy framework. You will learn how to efficiently enumerate and sample graphs with specific constraints. No longer will you be limited by the complexities of combinatorics.
Don't miss out on the opportunity to expand your knowledge and advance your network analysis skills. Discover the power of maximum-entropy models by getting your hands on Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics. Click here to order now.
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