Graph database Graph databases are part of the NoSQL databases created to address the limitations of the existing relational databases. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model[according to whom?] in relational systems. Graph databases differ from graph compute engines. Background Graph databases, on the other hand, portrays the data as it is viewed conceptually. Graph Graph databases employ nodes, properties, and edges. A graph within graph databases is based on graph theory. Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked. Graph models Labeled-property graph A labeled-property graph model is represented by a set of nodes, relationships, properties, and labels. Graph types
Graph structure in the web Andrei Broder1, Ravi Kumar2, Farzin Maghoul1, Prabhakar Raghavan2, Sridhar Rajagopalan2, Raymie Stata3, Andrew Tomkins2, Janet Wiener3 1: AltaVista Company, San Mateo, CA. 2: IBM Almaden Research Center, San Jose, CA. 3: Compaq Systems Research Center, Palo Alto, CA. Abstract The study of the web as a graph is not only fascinating in its own right, but also yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the web graph using two Altavista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the web is considerably more intricate than suggested by earlier experiments on a smaller scale. Keywords: graph structure, diameter, web measurement 1. Designing crawl strategies on the web [Cho and Garcia-Molina 2000]. 1.1 Our main results 1.2 Related prior work
Data Recipes phpCallGraph - A Static Call Graph Generator for PHP Six Degrees of Wikipedia Six Degrees of Wikipedia is a Harvard CS205 class project aimed at analyzing and understanding the structure of the English Wikipedia . The project was inspired by similar projects such as the Oracle of Bacon (which modeled the popular trivia game Six Degrees of Kevin Bacon ), Microsoft Academic Map (which visualized the concept of Erdos numbers ) and a 2008 version of Six Degrees of Wikipedia . Similar to these projects, our analysis goes beyond exploring the Six Degrees of Separation concept and attempts to provide insights into the overall graph structure of Wikipedia. The following 2-minute video presents the major components of the project: Is Wikipedia really a graph? The English Wikipedia is composed of almost 6 million different pages. Learning more about the project Visit the Graph Extraction Section to learn how the project extracted the Wikipedia graph by processing a 50GB dump of the English Wikipedia database obtained from that Freebase Wikipedia Extraction (WEX) project.
Welcome To Apache Incubator Giraph HyperGraphDB - A Graph Database HyperGraphDB is a general purpose, extensible, portable, distributed, embeddable, open-source data storage mechanism. It is a graph database designed specifically for artificial intelligence and semantic web projects, it can also be used as an embedded object-oriented database for projects of all sizes. The system is reliable and in production use is several projects, including a search engine and our own Seco scripting IDE where most of the runtime environment is automatically saved as a hypergraph. HyperGraphDB is primarily what its carefully chosen name implies: a database for storing hypergraphs. While it falls into the general family of graph databases, it is hard to categorize HyperGraphDB as yet another database because much of its design evolves around providing the means to manage structure-rich information with arbitrary layers of complexity. Key Facts Possible Usage Scenarios Semantic Web projects are an obvious domain of application of HyperGraphDB.
Aurelius | Applying Graph Theory and Network Science Pregel Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing.