  Hidden graphs are flexible abstractions that are composed of a set of known
vertices (nodes), whereas the set of edges are not known in advance. To uncover
the set of edges, multiple edge probing queries must be executed by evaluating
a function $f(u,v)$ that returns either true or false, if nodes $u$ and $v$ are
connected or not respectively. Evidently, the graph can be revealed completely
if all possible $n(n-1)/2$ probes are executed for a graph containing $n$
nodes. However, the function $f()$ is usually computationally intensive and
therefore executing all possible probing queries result in high execution
costs. The target is to provide answers to useful queries by executing as few
probing queries as possible. In this work, we study the problem of discovering
the top-$k$ nodes of a hidden bipartite graph with the highest degrees, by
using distributed algorithms. In particular, we use Apache Spark and provide
experimental results showing that significant performance improvements are
achieved in comparison to existing centralized approaches.
