On Counting Triangles through Edge Sampling in Large Dynamic Graphs

Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information. To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (ESD). It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions. We provide a comparative analysis of the performance of ESD against two current state-of-the-art algorithms in terms of accuracy and complexity. The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better. We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017

RELATIVE PRICE DYNAMICS AND MONETARY POLICY: EVIDENCE FROM DIRECTED GRAPHS

This paper examines the dynamic relationship between monetary policy variables and agricultural prices using alternative VAR-type model specifications. Time series techniques as currently specified in most studies raises issues of misspecification and inferential adequacy because observational (non-experimental) data are being analyzed by estimations techniques better suited for experimental data. Directed graph theory is proposed as an alternative modeling approach to supplement current methods of analyzing agricultural time series.Financial Economics,

SU(3) lattice gauge theory with a mixed fundamental and adjoint plaquette action: Lattice artefacts

We study the four-dimensional SU(3) gauge model with a fundamental and an adjoint plaquette term in the action. We investigate whether corrections to scaling can be reduced by using a negative value of the adjoint coupling. To this end, we have studied the finite temperature phase transition, the static potential and the mass of the 0^{++} glueball. In order to compute these quantities we have implemented variance reduced estimators that have been proposed recently. Corrections to scaling are analysed in dimensionless combinations such as T_c/\sqrt{\sigma} and m_{0^{++}}/T_c. We find that indeed the lattice artefacts in e.g. m_{0^{++}}/T_c can be reduced considerably compared with the pure Wilson (fundamental) gauge action at the same lattice spacing.Comment: 36 pages, 12 figure

Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem