  Graph pattern matching is a fundamental operation for the analysis and
exploration ofdata graphs. In thispaper, we presenta novel approachfor
efficiently finding homomorphic matches for hybrid graph patterns, where each
pattern edge may be mapped either to an edge or to a path in the input data,
thus allowing for higher expressiveness and flexibility in query formulation. A
key component of our approach is a lightweight index structure that leverages
graph simulation to compactly encode the query answer search space. The index
can be built on-the-fly during query execution and does not have to be
persisted to disk. Using the index, we design a multi-way join algorithm to
enumerate query solutions without generating any potentially exploding
intermediate results. We demonstrate through extensive experiments that our
approach can efficiently evaluate a wide range / broad spectrum of graph
pattern queries and greatly outperforms existing approaches and recent graph
query engines/systems.
