  Graph analytics for large scale graphs has gained interest in recent years.
Many graph algorithms have been designed for vertex-centric distributed graph
processing frameworks to operate on large graphs with 100 M vertices and edges,
using commodity clusters and Clouds. Subgraph-centric programming models have
shown additional performance benefits than vertex-centric models. But direct
mapping of vertex-centric and shared-memory algorithms to subgraph-centric
frameworks are either not possible, or lead to inefficient algorithms. In this
paper, we present three subgraph-centric distributed graph algorithms for
triangle counting, clustering and minimum spanning forest, using variations of
shared and vertex-centric models. These augment existing subgraph-centric
algorithms that exist in literature, and allow a broader evaluation of these
three classes of graph processing algorithms and platforms.
