In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …

RDF has seen increased adoption in recent years, prompting the standardization of the
SPARQL query language for RDF, and the development of local and distributed engines for …

The performance bottlenecks of graph applications depend not only on the algorithm and
the underlying hardware, but also on the size and structure of the input graph. As a result …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Graph data modeling and querying arises in many practical application domains such as
social and biological networks where the primary focus is on concepts and their …

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 …

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 …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …