Expressive languages for selecting groups from graph-structured data

Abstract

Many query languages for graph-structured data are based on regular path expressions, which describe relations among pairs of nodes. We propose an extension that allows to retrieve groups of nodes based on group structural characteristics and relations to other nodes or groups. It allows to express group selection queries in a concise and natural style, and can be integrated into any query language based on regular path queries. We present an efficient algorithm for evaluating group queries in polynomial time from an input data graph. Evaluations using real-world social networks demonstrate the practical feasibility of our approach.

Big Graph Analyses: From Queries to Dependencies and Association Rules

“…QGRAPH [74] annotates nodes and edges with a counting range (count 0 as negated edge) to specify the number of matches that must exist in a database. Set regular path queries (SRPQ) [50] extends regular path queries with quantification for group selection, to restrict the nodes in one set connected to the nodes of another. For social networks, SocialScope [3] and SNQL [53] define algebraic languages with numeric aggregates on node and edge sets.…”

“…In contrast, previous proposals do not allow at least one of these. (2) We focus on graph pattern queries, beyond set regular expressions [50] and rules of [98]. (3) We show that quantified matching is DPcomplete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [102].…”

Adding Counting Quantifiers to Graph Patterns

“…QGRAPH [10] annotates nodes and edges with a counting range (count 0 as negated edge) to specify the number of matches that must exist in a database. Set regular path queries (SRPQ) [31] extends regular path queries with quantification for group selection, to restrict the nodes in one set connected to the nodes of another. For social networks, SocialScope [5] and SNQL [32] are algebraic languages with numeric aggregates on node and edge sets.…”

“…In contrast, previous proposals do not allow at least one of these. (2) We focus on graph pattern queries, which are widely used in social media marketing and knowledge discovery; they are beyond set regular expressions [31] and rules of [30]. (3) Quantified matching with QGPs is DP-complete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [33].…”

“…(3) Quantified matching with QGPs is DP-complete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [33]. In contrast, SPARQL and SPARQLog are PSPACE-hard [28], and SRPQ takes EXPTIME [31]; while the complexity bounds for QGRAPH [10], SocialScope [5] and SNQL [32] are unknown, they are either more expensive than QGPs (e.g., QGRAPH is a fragment of FO(count)), or cannot express numeric and ratio quantifiers [5,32]. (4) No prior work has studied parallel scalable algorithms for its queries.…”