Using a coupling argument, we establish a general weak law of large numbers for functionals of binomial point processes in d-dimensional space, with a limit that depends explicitly on the (possibly non-uniform) density of the point process. The general result is applied to the minimal spanning tree, the k-nearest neighbors graph, the Voronoi graph, and the sphere of influence graph. Functionals...

Long Method is a code smell that frequently happens in software development, which refers to the complex method with multiple functions. Detecting and refactoring such problems has been popular topic refactoring, many detection approaches have proposed. In past years, based on metrics or rules leading way long detection. However, approach deep learning also attracted extensive attention recent ...

Representing manifolds using fewer examples has the advantages of eliminating the influence of outliers and noisy points and simultaneously accelerating the evaluation of predictors learned from the manifolds. In this paper, we give the definition of manifold-preserving sparse graphs as a representation of sparsified manifolds and present a simple and efficient manifold-preserving graph reducti...

a set $s$ of vertices in a graph $g=(v,e)$ is called a total$k$-distance dominating set if every vertex in $v$ is withindistance $k$ of a vertex in $s$. a graph $g$ is total $k$-distancedomination-critical if $gamma_{t}^{k} (g - x) < gamma_{t}^{k}(g)$ for any vertex $xin v(g)$. in this paper,we investigate some results on total $k$-distance domination-critical of graphs.

In this paper, we develop semi-external and external memory algorithms for graph partitioning and clustering problems. Graph partitioning and clustering are key tools for processing and analyzing large complex networks. We address both problems in the (semi-)external model by adapting the size-constrained label propagation technique. Our (semi-)external size-constrained label propagation algori...

Scalable Graph-Based Learning Applied to Human Language Technology Andrei Alexandrescu Chair of the Supervisory Committee: Associate Research Professor Katrin Kirchhoff Electrical Engineering Graph-based semi-supervised learning techniques have recently attracted increasing attention as a means to utilize unlabeled data in machine learning by placing data points in a similarity graph. However, ...