Inferring Hidden Structures in Random Graphs

Random graphs with hidden color.

We propose and investigate a unifying class of sparse random graph models, based on a hidden coloring of edge-vertex incidences, extending an existing approach, random graphs with a given degree distribution, in a way that admits a nontrivial correlation structure in the resulting graphs. The approach unifies a number of existing random graph ensembles within a common general formalism, and all...

Properties of random graphs with hidden color.

We investigate in some detail a recently suggested general class of ensembles of sparse undirected random graphs based on a hidden stub coloring, with or without the restriction to nondegenerate graphs. The calculability of local and global structural properties of graphs from the resulting ensembles is demonstrated. Cluster size statistics are derived with generating function techniques, yield...

Inferring Hidden Causal Structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observ...

Inferring Hidden Causes

One of the important aspects of human causal reasoning is that from the time we are young children we reason about unobserved causes. How can we learn about unobserved causes from information about observed events? Causal Bayes nets provide a formal account of how causal structure is learned from a combination of associations and interventions. This formalism makes specific predictions about th...

Dissertation INFERRING HIDDEN FEATURES IN THE INTERNET