{"product_id":"reconstructing-networks","title":"Reconstructing Networks","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eNetwork reconstruction is a field that deals with the problem of missing information in complex networks. It involves inferring interactions, mesoscale properties, and microscopic connections based on statistical physics and information theory. The discussion is organized according to different scales of the reconstruction task. \u003c\/blockquote\u003e\u003cp\u003e\n                                                            \u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\n                              \u003cstrong\u003eLength\u003c\/strong\u003e: 75 pages\u003cbr\u003e\n                              \u003cstrong\u003ePublication date\u003c\/strong\u003e: 09 September 2021\u003cbr\u003e\n                              \u003cstrong\u003ePublisher\u003c\/strong\u003e: Cambridge University Press\u003cbr\u003e\n                          \u003c\/p\u003e \u003cp\u003e\u003cbr\u003eNetwork reconstruction is a crucial task in the analysis of complex networks, as datasets often contain missing information, such as interactions data that have not been measured or discovered. This missing information can be affected by errors, or hidden due to privacy issues. In this Element, we provide an overview of the ideas, methods, and techniques used to address this problem, which together form the field of network reconstruction. Given the vast scope of the subject, the authors focus on inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, such as reconstructing the macroscopic structure of the network, inferring its mesoscale properties, or predicting individual microscopic connections.\u003cbr\u003e\u003cbr\u003eNetwork reconstruction is a complex and challenging task that requires a combination of various ideas, methods, and techniques. Here, we will explore some of the key concepts and approaches used in network reconstruction.\u003cbr\u003e\u003cbr\u003eOne of the fundamental challenges in network reconstruction is the presence of missing information. Interactions data that have not been measured or discovered can lead to errors in the reconstruction process. Additionally, privacy issues can prevent the access to certain data, making it difficult to reconstruct the network accurately.\u003cbr\u003e\u003cbr\u003eTo address these challenges, researchers have developed a range of methods and techniques. One popular approach is network inference, which involves using statistical models and machine learning algorithms to infer the missing information from the available data. These methods can be used to reconstruct networks at different scales, from the macroscopic structure to the individual microscopic connections.\u003cbr\u003e\u003cbr\u003eStatistical physics and information theory are two key areas that contribute to network reconstruction. Statistical physics provides insights into the structure and dynamics of complex systems, while information theory deals with the transmission and processing of information. These fields are used to develop models and algorithms that can reconstruct networks with high accuracy and reliability.\u003cbr\u003e\u003cbr\u003eOne of the most commonly used network inference methods is graph theory, which is a branch of mathematics that studies the structure and properties of graphs. Graphs are a fundamental representation of networks, and they can be used to reconstruct networks with high accuracy. Graph-based methods can be used to infer missing edges, node attributes, and other network properties.\u003cbr\u003e\u003cbr\u003eAnother important area of network reconstruction is network embedding, which involves mapping the nodes of a network into a lower-dimensional space. This can be done using various techniques, such as deep learning, which is a powerful machine learning algorithm that can learn complex patterns in data. Network embedding can be used to reduce the dimensionality of the network, making it easier to analyze and interpret.\u003cbr\u003e\u003cbr\u003eIn addition to these methods, network reconstruction also involves the use of optimization techniques, such as gradient descent and evolutionary algorithms. These techniques are used to find the optimal solution to the reconstruction problem, which can be challenging due to the complexity of the network and the presence of missing information.\u003cbr\u003e\u003cbr\u003eNetwork reconstruction has a wide range of applications in various fields, such as social networks, biological networks, and transportation networks. For example, social networks can be used to study the spread of diseases, while biological networks can be used to study the interactions between genes and proteins. Transportation networks can be used to optimize traffic flow.\u003cbr\u003e\u003cbr\u003eIn conclusion, network reconstruction is a crucial task in the analysis of complex networks, as it allows researchers to gain insights into the structure and dynamics of these networks. The presence of missing information and privacy issues pose significant challenges, but the development of network inference methods and techniques has made it possible to reconstruct networks with high accuracy and reliability. Statistical physics and information theory are two key areas that contribute to network reconstruction, and graph theory and network embedding are two popular methods used to reconstruct networks. Optimization techniques are also used to find the optimal solution to the reconstruction problem. With the increasing availability of data and the development of new technologies, network reconstruction will continue to play an important role in advancing our understanding of complex systems.\u003c\/p\u003e\u003cp\u003e\n                            \u003cstrong\u003eWeight\u003c\/strong\u003e: 168g\n                            \u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 227 x 229 x 9 (mm)\n                            \u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781108726818\n                            \n                          \u003c\/p\u003e","brand":"Giulio Cimini,Rossana Mastrandrea,Tiziano Squartini","offers":[{"title":"Paperback \/ softback","offer_id":44095070568698,"sku":"9781108726818","price":17.14,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/shulphink.com\/products\/reconstructing-networks","provider":"Shulph Ink","version":"1.0","type":"link"}