Measuring Networks
We have adopted the view of graphs and, more generally, cell complexes as a domain upon which we may apply the tools of calculus to formulate differential equations and to analyze data. An important aspect of the discrete differential operators is that the operators are defined by the topology of th...
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Published in | Discrete Calculus pp. 267 - 289 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
London
Springer London
2010
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Subjects | |
Online Access | Get full text |
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Abstract | We have adopted the view of graphs and, more generally, cell complexes as a domain upon which we may apply the tools of calculus to formulate differential equations and to analyze data. An important aspect of the discrete differential operators is that the operators are defined by the topology of the domain itself. Therefore, in an effort to provide a complete treatment of these differential operators, we examine in this chapter the properties of the network which may be extracted from the structure of these operators. In addition to the network properties extracted directly from the differential operators, we also review other methods for measuring the structural properties of a network. Specifically, the properties of the network that we consider are based on distances, partitioning, geometry, and topology. Our particular focus will be on the measurement of these properties from the graph structure. Applications will illustrate the use of these measures to predict the importance of nodes and to relate these measures to other properties of the subject being modeled by the network. |
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AbstractList | We have adopted the view of graphs and, more generally, cell complexes as a domain upon which we may apply the tools of calculus to formulate differential equations and to analyze data. An important aspect of the discrete differential operators is that the operators are defined by the topology of the domain itself. Therefore, in an effort to provide a complete treatment of these differential operators, we examine in this chapter the properties of the network which may be extracted from the structure of these operators. In addition to the network properties extracted directly from the differential operators, we also review other methods for measuring the structural properties of a network. Specifically, the properties of the network that we consider are based on distances, partitioning, geometry, and topology. Our particular focus will be on the measurement of these properties from the graph structure. Applications will illustrate the use of these measures to predict the importance of nodes and to relate these measures to other properties of the subject being modeled by the network. |
Author | Grady, Leo J. Polimeni, Jonathan R. |
Author_xml | – sequence: 1 givenname: Leo J. surname: Grady fullname: Grady, Leo J. email: leo.grady@siemens.com organization: Siemens Corporate Research, Princeton, USA – sequence: 2 givenname: Jonathan R. surname: Polimeni fullname: Polimeni, Jonathan R. email: jonp@nmr.mgh.harvard.edu organization: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA |
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Copyright | Springer-Verlag London Limited 2010 |
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DOI | 10.1007/978-1-84996-290-2_8 |
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SubjectTerms | Average Path Length Betti Number Cluster Coefficient Laplacian Matrix Wiener Index |
Title | Measuring Networks |
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