Graph Database Indexing Using Structured Graph Decomposition

Abstract

We introduce a novel method of indexing graph databases in order to facilitate subgraph isomorphism and similarity queries. The index is comprised of two major data structures. The primary structure is a directed acyclic graph which contains a node for each of the unique, induced subgraphs of the database graphs. The secondary structure is a hash table which crossindexes each subgraph for fast isomorphic lookup. In order to create a hash key independent of isomorphism, we utilize a code-based canonical representation of adjacency matrices, which we have further refined to improve computation speed. We validate the concept by demonstrating its effectiveness in answering queries for two practical datasets. Our experiments show that for subgraph isomorphism queries, our method outperforms existing methods by more than an order of magnitude.

Connected substructure similarity search

“…Due to NP-completeness, techniques for processing a large number of data graphs follow the filtering-verification paradigm. Most work in subgraph containment search focuses on developing effective and efficient indexing techniques, including, GraphGrep [10], gIndex [23], Closure-Tree [11], TreePi [26], gString [13], a graph decomposition based index [21], FGIndex [6], Tree+δ [27], GCoding [28], etc. In [5], cIndex is developed for the reverse version of subgraph containment search, namely super-graph containment search .…”

“…In [11], Closure-Tree techniques have been extended to identify the K data graphs which are the most nearly isomorphic to the query graph; that is, full-graph similarity search. In [21], a variation of substructure similarity search, with a stronger constraint, is proposed. It aims to find data graphs with the minimum number of miss-matchings of vertex and edge labels bounded by a given threshold and disallows the size of a query graph greater than that of data graph.…”

“…It aims to find data graphs with the minimum number of miss-matchings of vertex and edge labels bounded by a given threshold and disallows the size of a query graph greater than that of data graph. Due to inherent difference, techniques in [11,21] are not applicable to the problem studied in the paper. Moreover, they do not exclude disconnected structure matchings.…”

Answering pattern match queries in large graph databases via graph embedding

“…In order to conduct effective analysis over graphs, various types of queries have been investigated, such as shortest path query [9,18,7], reachability query [9,29,27,5], and subgraph query [24,34,6,17,31,36,15,25]. These are all interesting, but in this paper, we focus on pattern match queries, since they are more flexible than subgraph queries and more informative than simple shortest path or reachability queries.…”

“…Specifically, we need efficient pruning strategies to reduce the search space. Although many effective pruning techniques have been proposed for subgraph search (e.g., [24,34,6,17,31,36,15,25]), they can not be applied to pattern match queries since these pruning rules are based on the necessary condition of subgraph isomorphism. We propose a novel and effective method to reduce the search space significantly.…”