  The hidden ancestor graph is a new stochastic model for a vertex-labelled
multigraph $G$ in which the observable vertices are the leaves $L$ of a random
rooted tree $T$, whose edges and non-leaf nodes are hidden. The likelihood of
an edge in $G$ between two vertices in $L$ depends on the height of their
lowest common ancestor in $T$. The label of a vertex $v \in L$ depends on a
randomized label inheritance mechanism within $T$ such that vertices with the
same parent often have the same label. High label assortativity, high average
local clustering, heavy tailed vertex degree distribution, and sparsity, can
all coexist in this model. The agreement edges (end point labels agree) and the
conflict edges (end point labels differ) constitute complementary subgraphs,
useful for testing anomaly correction algorithms. Instances with a hundred
million edges can easily be built on a workstation in minutes.
