Graph database models can be defined as those in which data structures for the schema
and instances are modeled as graphs or generalizations of them, and data manipulation is …

Provenance in the context of workflows, both for the data they derive and for their
specification, is an essential component to allow for result reproducibility, sharing, and …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

The limitations of traditional databases, in particular the relational model, to cover the
requirements of current applications has lead the development of new database …

Graph mining is an important research area within the domain of data mining. The field of
study concentrates on the identification of frequent subgraphs within graph data sets. The …

The generation of RDF data has accelerated to the point where many data sets need to be
partitioned across multiple machines in order to achieve reasonable performance when …

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 …

Schema and ontology matching is a critical problem in many application domains, such as
semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many …

Graph has become increasingly important in modelling complicated structures and
schemaless data such as proteins, chemical compounds, and XML documents. Given a …

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 …