Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...
We introduce the (private) entropy of a directed graph (in a new network coding sense) as well as a number of related concepts. We show that the entropy of a directed graph is ide...
Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of sim...
Michalis Potamias, Francesco Bonchi, Aristides Gio...
Many real–world networks show a scale–free degree distribution, a structure that is known to be very stable in case of random failures. Unfortunately, the very same structure ...
Abstract-- This demonstration presents an interactive provenance browser for visualizing and querying data dependency (lineage) graphs produced by scientific workflow runs. The bro...