—In this paper, we study node connectivity in multi-hop wireless networks. Nodal degree of connectivity as one of the fundamental graph properties is the basis for the study of n...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Visual sensors provide exclusively uncertain and partial knowledge of a scene. In this article, we present a suitable scene knowledge representation that makes integration and fusi...
We propose an efficient and novel approach for discovering communities in real-world random networks. Communities are formed by subsets of nodes in a graph, which are closely rela...
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density func...