Over the past couple of decades, the explosion of densely interconnected data has
stimulated the research, development and adoption of graph database technologies. From …

There are two types of high-performance graph processing engines: low-and high-level
engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures …

Graph analysis is a powerful method in data analysis. Although several frameworks have
been proposed for processing large graph instances in distributed environments, their …

We present Teseo, a new system for the storage and analysis of dynamic structural graphs
in main-memory and the addition of transactional support. Teseo introduces a novel design …

In this paper, we present Vertexica, a graph analytics tools on top of a relational database,
which is user friendly and yet highly efficient. Instead of constraining programmers to SQL …

Graph analytics is becoming increasingly popular, with a number of new applications and
systems developed in the past few years. In this paper, we study Vertica relational database …

With our increasing reliance on the correct functioning of computer systems, identifying and
eliminating vulnerabilities in program code is gaining in importance. To date, the vast …

This paper presents an empirical study of how a wide spectrum of systems handle the graph
pattern matching problem. Our approach is to take the well-known LUBM benchmark, model …

Graph databases with a custom non-relational backend promote themselves to outperform
relational databases in answering queries on large graphs. Recent empirical studies show …

Recently graph has been drawing lots of attention both as a natural data model that captures
fine-grained relationships between data entities and as a tool for powerful data analysis that …