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Related:  Graph theory, graph practice

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.

Graph database Description[edit] Graph databases employ nodes, properties, and edges. Graph databases are based on graph theory, and employ nodes, edges, and properties. Nodes represent entities such as people, businesses, accounts, or any other item to be tracked. Jeremy Zawodny's blog I found myself reading NoSQL is a Premature Optimization a few minutes ago and threw up in my mouth a little. That article is so far off base that I’m not even sure where to start, so I guess I’ll go in order. In fact, I would argue that starting with NoSQL because you think you might someday have enough traffic and scale to warrant it is a premature optimization, and as such, should be avoided by smaller and even medium sized organizations. You will have plenty of time to switch to NoSQL as and if it becomes helpful. Until that time, NoSQL is an expensive distraction you don’t need.

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.

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.

AllegroGraph News August 2011 AllegroGraph News November, 2013 In this issue Free Webcast: Augmenting Hadoop for Graph Analytics Wednesday, November 20 - 10:00 AM Pacific Interactome Part of the DISC1 interactome with genes represented by text in boxes and interactions noted by lines between the genes. From Hennah and Porteous, 2009.[1] The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq.[2] Though interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs. Molecular interaction networks[edit]

Connections in Time Some relationships change over time. Think about your friends from high school, college, work, the city you used to live in, the ones that liked you ex- better, etc. When exploring a social network it is important that we understand not only the strength of the relationship now, but over time. We can use communication between people as a measure. I ran into a visualization that explored how multiple parties where connected by communications in multiple projects. Database Models: Hierarcical, Network, Relational, Object-Oriented, Semistructured, Associative and Context. The context data model combines features of all the above models. It can be considered as a collection of object-oriented, network and semistructured models or as some kind of object database. In other words this is a flexible model, you can use any type of database structure depending on task. Such data model has been implemented in DBMS ConteXt. The fundamental unit of information storage of ConteXt is a CLASS. Class contains METHODS and describes OBJECT.