Graph Topology Abstraction for Distributed Path Queries

Querying graph data often involves identifying matching paths, either as an end product, or as an intermediate step for further graph analysis. Distributed graph querying, suffers from high communication to computation costs, due to challenges in constructing comprehensive structural indexes. This could result in severe performance degradation in terms of turnaround time, which often worsens with increasing graph size and density. In this paper, we propose a novel topology abstraction layer, that helps improve query response time by reducing the communication overhead for selective exploration of large distributed graphs. We demonstrate the effectiveness of our model and also go on to show that our abstraction layer works well in both data-parallel and graph-parallel paradigms.