Conditional Graph Pattern Matching with a Basic Static Analysis

Abstract

We propose conditional graph patterns ( CGPs ) that extend conventional ones with simple counting quantifiers on edges, attributes on nodes, positive and negative predicates. In emerging applications such as social network marketing, CGPs allow to express complex search conditions and to find more sensible information than their traditional counterparts. We show that the CGPs expressivity does not come with a much higher price. Indeed, we propose a matching algorithm that is based on a revised notion of graph simulation and allows to match CGPs over any data graphs in quadratic time, as opposed to the prohibitive solutions based on subgraph isomorphism. We discuss a parallel version of our algorithm that makes it very efficient over real-life data. We investigate the satisfiability and containment problems of CGPs and we show that they are in quadratic time by providing a non-trivial checking algorithm for each one. This paper is the first effort that investigates static analysis of non-conventional graph patterns containing important features that are widely used in practice. An extensive experimental study has been conducted to show effectiveness and efficiency of our results.

Revisited Containment for Graph Patterns

“…When considering data and patterns modeled as graphs, evaluating a graph pattern Q 2 over a data graph G by simulation [16] and all its variants [2,[11][12][13][14] yields a match result defined as a function that maps each node (resp. edge) of Q 2 to all its matches in G. Therefore, we revise the traditional definition of containment for CGPs in terms of match result as follows.…”