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 …

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 …

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 …

Graph-based text representation is one of the important preprocessing steps in data and text
mining, Natural Language Processing (NLP), and information retrieval approaches. The …

The explosion in the amount of the available RDF data has lead to the need to explore,
query and understand such data sources. Due to the complex structure of RDF graphs and …

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 …

Graph data have become ubiquitous and manipulating them based on similarity is essential
for many applications. Graph edit distance is one of the most widely accepted measures to …

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 …