HyGraph: a subgraph isomorphism algorithm for efficiently querying big graph databases

The big graph database provides strong modeling capabilities and efficient querying for complex applications. Subgraph isomorphism which finds exact matches of a query graph in the database efficiently, is a challenging problem. Current subgraph isomorphism approaches mostly are based on the pruning...

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Bibliographic Details
Published inJournal of big data Vol. 9; no. 1; pp. 1 - 22
Main Authors Asiler, Merve, Yazıcı, Adnan, George, Roy
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 21.04.2022
Springer Nature B.V
SpringerOpen
Subjects
Algorithms
Big Data
Communications Engineering
Complexity
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Exact matching algorithm
Graph database
Graph theory
Information Storage and Retrieval
Isomorphism
Mathematical Applications in Computer Science
Neo4j databases
Networks
Queries
Query graph search
Soccer
Subgraph isomorphism problem
Query graph search
Graph database
Exact matching algorithm
Neo4j databases
Subgraph isomorphism problem
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Summary:The big graph database provides strong modeling capabilities and efficient querying for complex applications. Subgraph isomorphism which finds exact matches of a query graph in the database efficiently, is a challenging problem. Current subgraph isomorphism approaches mostly are based on the pruning strategy proposed by Ullmann. These techniques have two significant drawbacks- first, they are unable to efficiently handle complex queries, and second, their implementations need the large indexes that require large memory resources. In this paper, we describe a new subgraph isomorphism approach, the HyGraph algorithm, that is efficient both in querying and with memory requirements for index creation. We compare the HyGraph algorithm with two popular existing approaches, GraphQL and Cypher using complexity measures and experimentally using three big graph data sets—(1) a country-level population database, (2) a simulated bank database, and (3) a publicly available World Cup big graph database. It is shown that the HyGraph solution performs significantly better (or equally) than competing algorithms for the query operations on these big databases, making it an excellent candidate for subgraph isomorphism queries in real scenarios.
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-022-00589-0