Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it
impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed …

Due to its simplicity, efficiency, and efficacy, naive Bayes (NB) has continued to be one of the
top 10 algorithms in the data mining and machine learning community. Of numerous …

In this paper, we study the problem of subgraph matching that extracts all subgraph
isomorphic embeddings of a query graph q in a large data graph G. The existing algorithms …

Subgraph matching (or subgraph isomorphism) is one of the fundamental problems in graph
analysis. Extensive research has been done to develop practical solutions for subgraph …

Given a query graph q and a data graph g, the subgraph isomorphism search finds all
occurrences of q in g and is considered one of the most fundamental query types for many …

We study the performance of eight representative in-memory subgraph matching algorithms.
Specifically, we put QuickSI, GraphQL, CFL, CECI, DP-iso, RI and VF2++ in a common …

Finding subgraph isomorphisms is an important problem in many applications which deal
with data modeled as graphs. While this problem is NP-hard, in recent years, many …

Subgraph matching finds all distinct isomorphic embeddings of a query graph on a data
graph. For large graphs, current solutions face the scalability challenge due to expensive …

The dramatic proliferation of sophisticated networks has resulted in a growing need for
supporting effective querying and mining methods over such large-scale graph-structured …

We study the problem of optimizing subgraph queries using the new worst-case optimal join
plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time …