By Ernesto Estrada, Philip A. Knight
The learn of community thought is a hugely interdisciplinary box, which has emerged as an immense subject of curiosity in quite a few disciplines starting from physics and arithmetic, to biology and sociology. This ebook promotes the various nature of the learn of advanced networks through balancing the wishes of scholars from very various backgrounds. It references the main commonplace suggestions in community conception, provides examples in their purposes in fixing sensible difficulties, and transparent symptoms on the best way to examine their effects. within the first a part of the ebook, scholars and researchers will become aware of the quantitative and analytical instruments essential to paintings with advanced networks, together with the main easy innovations in community and graph conception, linear and matrix algebra, in addition to the actual recommendations most often used for learning networks. they're going to additionally locate guide on a few key talents corresponding to how one can facts analytic effects and the way to govern empirical community info. the majority of the textual content is targeted on educating readers at the most respected instruments for contemporary practitioners of community idea. those contain measure distributions, random networks, community fragments, centrality measures, clusters and groups, communicability, and native and international houses of networks. the mix of conception, instance and approach which are offered during this textual content, should still prepared the scholar to behavior their very own research of networks with self belief and make allowance lecturers to pick applicable examples and difficulties to educate this topic within the classroom. Read more...
summary: The research of community thought is a hugely interdisciplinary box, which has emerged as a tremendous subject of curiosity in numerous disciplines starting from physics and arithmetic, to biology and sociology. This ebook promotes the varied nature of the examine of advanced networks by means of balancing the desires of scholars from very diverse backgrounds. It references the main widespread innovations in community idea, presents examples in their purposes in fixing functional difficulties, and transparent symptoms on the way to examine their effects. within the first a part of the ebook, scholars and researchers will notice the quantitative and analytical instruments essential to paintings with advanced networks, together with the main simple strategies in community and graph concept, linear and matrix algebra, in addition to the actual thoughts most often used for learning networks. they'll additionally locate guideline on a few key talents corresponding to the best way to evidence analytic effects and the way to govern empirical community info. the majority of the textual content is concentrated on educating readers at the most precious instruments for contemporary practitioners of community idea. those comprise measure distributions, random networks, community fragments, centrality measures, clusters and groups, communicability, and native and international homes of networks. the combo of concept, instance and procedure which are provided during this textual content, may still prepared the coed to behavior their very own research of networks with self assurance and make allowance lecturers to choose acceptable examples and difficulties to coach this topic within the school room
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Additional info for A first course in network theory
2) y = ax ⇒ Y = bX + ln a, Y = ln y, X = ln x. b Suppose we are given data (x1 , y1 ), . . , (xn , yn ) for two variables. The basic principle of linear fitting is to find constants a and b so that the line y = ax + b matches the data as closely as possible. We do this by minimizing the errors ei = yi – axi – b over all choices of a and b. 3) i=1 to find the least squares solution, for which there are many efficient computational techniques. 46 Data Analysis and Manipulation If the relationship between the variables (or transformed variables) cannot be represented by a straight line then we can generalize the process and look for the best-fitting solution from a bigger class of functions (for example, polynomials of degree k, a sum of exponentials, or a trigonometric series).
Each rounding error is (relatively) tiny, and usually they will have a tiny effect on the answer. Ideally, we would like the size of the perturbations introduced prior to and during computations not to be amplified significantly by our method of solution. But if the problem we are trying to solve is badly conditioned then the errors introduced by rounding and discretization can be amplified massively and it is possible for the accumulation of only a few rounding errors to have a catastrophic effect.
While we may be able to control or mitigate errors, we should always ensure that the analytical techniques we use are sufficiently robust for us to be confident in our results. In this chapter, after presenting a brief taxonomy of experimental error, we look at some of the techniques available to us for processing and analysing data. 1 The modelling cycle Modelling error In almost every practical application of network theory, the network we analyse is an idealized model of a real-world situation that relies on certain assumptions holding either exactly or to within an accepted level of accuracy.