  The Mapper produces a compact summary of high dimensional data as a
simplicial complex. We study the problem of quantifying the interestingness of
subpopulations in a Mapper, which appear as long paths, flares, or loops.
First, we create a weighted directed graph G using the 1-skeleton of the
Mapper. We use the average values at the vertices of a target function to
direct edges (from low to high). The difference between the average values at
vertices (high-low) is set as the edge's weight. Covariation of the remaining h
functions (independent variables) is captured by a h-bit binary signature
assigned to the edge. An interesting path in G is a directed path whose edges
all have the same signature. We define the interestingness score of such a path
as a sum of its edge weights multiplied by a nonlinear function of their ranks
in the path.
  Second, we study three optimization problems on this graph G. In the problem
Max-IP, we seek an interesting path in G with the maximum interestingness
score. We show that Max-IP is NP-complete. For the special case when G is a
directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial
time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G.
  In the more general problem IP, the goal is to find a collection of
edge-disjoint interesting paths such that the overall sum of their
interestingness scores is maximized. We also study a variant of IP termed k-IP,
where the goal is to identify a collection of edge-disjoint interesting paths
each with k edges, and their total interestingness score is maximized. While
k-IP can be solved in polynomial time for k <= 2, we show k-IP is NP-complete
for k >= 3 even when G is a DAG. We develop polynomial time heuristics for IP
and k-IP on DAGs.
