Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landm...
Michalis Potamias, Francesco Bonchi, Carlos Castil...
Expanding a seed set into a larger community is a common procedure in link-based analysis. We show how to adapt recent results from theoretical computer science to expand a seed s...
We propose an approximation method to answer point-to-point shortest path queries in undirected edge-weighted graphs, based on random sampling and Voronoi duals. We compute a simp...
Shinichi Honiden, Michael E. Houle, Christian Somm...
In this paper, we propose an adaptive collusion attack to a block oriented watermarking scheme [1]. In this attack, traitors conspire to selectively manipulate watermarked blocks t...