Graph Bundling by Kernel Density Estimation
نویسندگان
چکیده
We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state-of-the-art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.
منابع مشابه
Cognitive Maps Exploration trough Kernel Density Estimation
Curently approximately 860,000 people are affected by Alzheimer's disease in France. This is why the study of Alzheimer’s disease has been identified as a major societal challenge. In the PAQUID cohort study, subjects performed a lexical evocation task by saying the maximum number of city names within 3 minutes. This task is directly related to the concept of cognitive map. The analysis of the ...
متن کاملParallel computation of kernel density estimates classifiers and their ensembles
Nonparametric supervised classifiers are interesting because they do not require distributional assumptions for the class conditional density, such as normality or equal covariance. However their use is not widespread because it takes a lot of time to compute them due to the intensive use of the available data. On the other hand bundling classifiers to produce a single one, known as an ensemble...
متن کاملComparison of the Gamma kernel and the orthogonal series methods of density estimation
The standard kernel density estimator suffers from a boundary bias issue for probability density function of distributions on the positive real line. The Gamma kernel estimators and orthogonal series estimators are two alternatives which are free of boundary bias. In this paper, a simulation study is conducted to compare small-sample performance of the Gamma kernel estimators and the orthog...
متن کاملThe Relative Improvement of Bias Reduction in Density Estimator Using Geometric Extrapolated Kernel
One of a nonparametric procedures used to estimate densities is kernel method. In this paper, in order to reduce bias of kernel density estimation, methods such as usual kernel(UK), geometric extrapolation usual kernel(GEUK), a bias reduction kernel(BRK) and a geometric extrapolation bias reduction kernel(GEBRK) are introduced. Theoretical properties, including the selection of smoothness para...
متن کاملIdentification of Hazardous Situations using Kernel Density Estimation Method Based on Time to Collision, Case study: Left-turn on Unsignalized Intersection
The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Comput. Graph. Forum
دوره 31 شماره
صفحات -
تاریخ انتشار 2012