Scalable supergraph search in large graph databases

Abstract

Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prunes false answers based on features in the pruning phase and performs subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationship to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a verification-free algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the high scalability of our algorithms.

Effect of Surrounding Atmospheres on Break Arc Durations of Electrical Contacts in DC Load Conditions

“…A series of experiments using a real-world dataset demonstrated the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best. In future work, we plan to compare DGTree [13] which also does not require any knowledge of the query set or the frequent subgraph patterns to construct its index but stores all mappings between graphs in the database and patterns in the index.…”

“…Graph searches are a fundamental component in applications such as chemo-informatics [19], [26], bioinformatics [1], computer-aided design [14], computer vision [16], [18], pattern recognition, XML, social networks, World Wide Web, and software analysis. For a database consisting of the set of graphs G = {g 1 , g 2 , · · · , g n } and a given query q, there are two types of graph searches depending on the desired output: [7], [8], [11], [17], [22], [24] • Supergraph search: {g i ∈ G | g i ⊆ q} [4], [13], [21], [23], [25].…”

Efficient Supergraph Search Using Graph Coding

“…• Subgraph search: {g i ∈ G | q ⊆ g i } [3], [7], [8], [11], [17], [22], [24] • Supergraph search: {g i ∈ G | g i ⊆ q} [4], [13], [21], [23], [25].…”

A Survey on Graph Database Management Techniques for Huge Unstructured Data

“…It also simplifies the complexity of design and implementation; popular notation is "if you can see whiteboard, you can graph." Being a high-level abstraction to the network model database, it reduced the coding effort to one-tenth; it's a key technology used in rapid application development (RAD) [5], [6].…”

“…It's portable and requires only a single JAR file for execution. DEX is called the fourth most popular graph database today [3], [6].…”