MultiGraphMatch: a subgraph matching algorithm for multigraphs

2025-01-16

AI Summary

This work addresses efficient subgraph matching on heterogeneous multigraphs featuring multiple edge types, labeled nodes, and multi-attribute nodes. To tackle the computational complexity inherent in such richly structured graphs, the authors propose a novel matching framework. Methodologically, it introduces (1) a bit-matrix data structure for fast edge-level matchability indexing and pruning; (2) a dynamic query-edge ordering strategy based on matchable-edge cardinality to substantially reduce the search space; and (3) an end-to-end matching engine capable of parsing and executing Cypher-style logical conditions. Extensive experiments on both synthetic and real-world multigraph datasets demonstrate that the approach achieves matching accuracy comparable to state-of-the-art systems—including SuMGra, Memgraph, and Neo4j—while matching or surpassing their performance, particularly under high-density, multi-relational scenarios.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Subgraph matching is the problem of finding all the occurrences of a small graph, called the query, in a larger graph, called the target. Although the problem has been widely studied in simple graphs, few solutions have been proposed for multigraphs, in which two nodes can be connected by multiple edges, each denoting a possibly different type of relationship. In our new algorithm MultiGraphMatch, nodes and edges can be associated with labels and multiple properties. MultiGraphMatch introduces a novel data structure called bit matrix to efficiently index both the query and the target and filter the set of target edges that are matchable with each query edge. In addition, the algorithm proposes a new technique for ordering the processing of query edges based on the cardinalities of the sets of matchable edges. Using the CYPHER query definition language, MultiGraphMatch can perform queries with logical conditions on node and edge labels. We compare MultiGraphMatch with SuMGra and graph database systems Memgraph and Neo4J, showing comparable or better performance in all queries on a wide variety of synthetic and real-world graphs.

Problem

• Subgraph Matching
• Efficiency Improvement
• Labeled Multi-Graphs

Innovation

• MultiGraphMatch
• BitMatrixStructure
• CYPHERSupport