Survey of graph database models

By Renzo Angles, Claudio Gutiérrez. 2008

1,774 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Representation Learning on Graphs: Methods and Applications (2017, background): “As a consequence of their ubiquity, graphs are the backbone of countless systems, allowing relational knowledge about interacting entities to be efficiently stored and accessed [2].”
Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012, background): “There has been substantial work [1] in graph structured databases dating back to the 1980’s along with many recent open-source and commercial products (e.”
A comparison of a graph database and a relational database: a data provenance perspective (2010, background): “… Yixin Chen Department of Computer and Information Science University of Mississippi ychen@cs.olemiss.edu Zhendong Zhao Department of Computer and Information Science University of Mississippi zzhao@olemiss.edu Dawn Wilkins Department of Computer and Information Science…”
Urban Computing: Concepts, Methodologies, and Applications (2014, background): “Static graph data management [Angles and Gutierrez 2008] has been studied intensively in database areas for years with many mature management systems available.”
Data management in cloud environments: NoSQL and NewSQL data stores (2013, background): “Graph databases are a suitable choice for the following types of applications: location-based services, recommendation engines, and complex network-based applications including social, information, technological, and biological networks [15,66].”
The Gremlin graph traversal machine and language (invited talk) (2015, background): “Graphs have seen a resurgence in the database community with the gro wth of graph database technology [1].”
TriAL (2018, background): “The simplest model of graph databases [4, 57], which dates back to References [18, 19], is that of directed edge-labeled graphs, i.e., pairs G = ( V , E ) , where V is a set of vertices (objects), and E is a set of labeled edges.”
Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012, background): “ high-level parallel and distributed frameworks. In this section we review related work in classic parallel abstractions, graph databases, and domain specic languages. There has been substantial work [1] in graph structured databases dating back to the 1980's along with many recent open-source ”