Distributed Algorithm for Relationship Queries on Large Graphs

Abstract

Massive-sized graph-structured data is now ubiquitous, e.g., social networks, databases, knowledge-bases, web-graphs, etc. An important class of queries on graph-structured data is "relationship queries". Essentially, given a set of entities (corresponding to nodes in the graph), finding a ranked list of interesting interconnections among them. While this problem has been studied for many years, the solutions proposed in the literature so far focus on the non-distributed setting. Clearly, such solutions will not scale with large graphs having billions of nodes and edges that are becoming commonplace. In this paper, we present an algorithm for keyword search on large graphs, which is based on the distributed parallel processing paradigm. We also analyze why our algorithm generates optimal answers. Finally, we report on preliminary empirical results of relationship queries on a subset of the Linked-Open Data graph.