arXiv subjects

Giovanni Micale

Publications and source records attributed to Giovanni Micale.

3 recordsLinked to original sources

arXiv2025

MultiGraphMatch: a subgraph matching algorithm for multigraphs

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.

cs.DB↗

arXiv2024

Mining Frequent Structures in Conceptual Models

The problem of using structured methods to represent knowledge is well-known in conceptual modeling and has been studied for many years. It has been proven that adopting modeling patterns represents an effective structural method. Patterns are, indeed, generalizable recurrent structures that can be exploited as solutions to design problems. They aid in understanding and improving the process of creating models. The undeniable value of using patterns in conceptual modeling was demonstrated in several experimental studies. However, discovering patterns in conceptual models is widely recognized as a highly complex task and a systematic solution to pattern identification is currently lacking. In this paper, we propose a general approach to the problem of discovering frequent structures, as they occur in conceptual modeling languages. As proof of concept, we implement our approach by focusing on two widely-used conceptual modeling languages. This implementation includes an exploratory tool that integrates a frequent subgraph mining algorithm with graph manipulation techniques. The tool processes multiple conceptual models and identifies recurrent structures based on various criteria. We validate the tool using two state-of-the-art curated datasets: one consisting of models encoded in OntoUML and the other in ArchiMate. The primary objective of our approach is to provide a support tool for language engineers. This tool can be used to identify both effective and ineffective modeling practices, enabling the refinement and evolution of conceptual modeling languages. Furthermore, it facilitates the reuse of accumulated expertise, ultimately supporting the creation of higher-quality models in a given language.

cs.AI↗

arXiv2020

MultiRI: Fast Subgraph Matching in Labeled Multigraphs

The Subgraph Matching (SM) problem consists of finding all the embeddings of a given small graph, called the query, into a large graph, called the target. The SM problem has been widely studied for simple graphs, i.e. graphs where there is exactly one edge between two nodes and nodes have single labels, but few approaches have been devised for labeled multigraphs, i.e. graphs having possibly multiple labels on nodes in which pair of nodes may have multiple labeled edges between them. Here we present MultiRI, a novel algorithm for the Sub-Multigraph Matching (SMM) problem, i.e. subgraph matching in labeled multigraphs. MultiRI improves on the state-of-the-art by computing compatibility domains and symmetry breaking conditions on query nodes to filter the search space of possible solutions. Empirically, we show that MultiRI outperforms the state-of-the-art method for the SMM problem in both synthetic and real graphs, with a multiplicative speedup between five and ten for large graphs, by using a limited amount of memory.

cs.DB↗