description: a representation of the interconnections among individuals within a network
122 results
by L.G. Meredith · 214pp · 14,382 words
.e. an image of, the graph of some underlying domain data structure, like the data structures of employee records in a payroll system or the social graph of a social media application Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 126 Section 6.1 Chapter 6 · Zippers and Contexts and URIs, Oh My
by Ryan Boyd · 29 Feb 2012 · 91pp · 18,831 words
scary step of handing over an account password. Types of functionality provided by OAuth-enabled APIs include the following: Getting access to a user’s social graph—their Facebook friends, people they’re following on Twitter, or their Google Contacts Sharing information about a user’s activities on your site by posting
by Sam Newman · 25 Dec 2014 · 540pp · 103,101 words
. For example, for a social network, we might store our users’ interactions in a graph-oriented database to reflect the highly interconnected nature of a social graph, but perhaps the posts the users make could be stored in a document-oriented data store, giving rise to a heterogeneous architecture like the one
by Jj Geewax · 19 Jul 2021 · 725pp · 168,262 words
see the graphical user interface for Facebook when we open a browser; however, we don’t have the ability to see how Facebook stores our social graph and other data. To use more formal terms for this aspect of visibility, we can say that the frontend (the part that all users see
by Pete Warden · 15 Feb 2011 · 39pp · 4,665 words
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Data Source Handbook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Websites WHOIS Blekko bit.ly Compete Delicious BackType PagePeeker People by Email WebFinger Flickr Gravatar Amazon AIM FriendFeed Google Social Graph MySpace Github Rapleaf Jigsaw People by Name WhitePages LinkedIn GenderFromName People by Account Klout Qwerly Search Terms BOSS 1 1 2 3 3 4 5
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.facebook.com/","iconUrl":"...","id":"facebook", "profileUrl":"http://www.facebook.com/profile.php?id=544591116", "name":"Facebook"}], "nickname":"timoreilly","id":"d85e8470-25c5-11dd-9ea1-003048343a40"} Google Social Graph Though it’s an early experiment that’s largely been superseded by Webfinger, this Google API can still be useful for the rich connection information
by Eric Redmond, Jim Wilson and Jim R. Wilson · 7 May 2012 · 713pp · 93,944 words
choose. Deciding which database to choose is often more complex than merely considering which genre maps best to a given domain’s data. Though a social graph may seem to clearly function best with a graph database, if you’re Facebook, you simply have far too much data to choose one. You
by Eric Voskuil, James Chiang and Amir Taaki · 28 Feb 2020 · 365pp · 56,751 words
loss of one node. A distributed (mesh) network is more resilient. A hybrid of these systems is considered decentralized. As a money Bitcoin forms a social graph. Only a person can decide to accept one money [203] or another in trade . A set of people sharing the same definition for a money
by Tom White · 29 May 2009 · 933pp · 205,691 words
linked-document collection. Google uses a vastly more refined version of this approach to identify top search hits. Identify celebrities and experts in the Twitter social graph. Users who have many more followers than their “trstrank” would imply are often spammers. Predict a school’s impact on student education, using millions of
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anonymized exam scores gathered over five years. Measuring Community The most interesting network in the Infochimps collection is a massive crawl of the Twitter social graph. With more than 90 million nodes, 2 billion edges, it is a marvelous instrument for understanding what people talk about and how they relate to
by Martin Kleppmann · 16 Mar 2017 · 1,237pp · 227,370 words
as nodes or entities) and edges (also known as relationships or arcs). Many kinds of data can be modeled as a graph. Typical examples include: Social graphs Vertices are people, and edges indicate which people know each other. The web graph Vertices are web pages, and edges indicate HTML links to other
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Your Postgres,” blog.heroku.com, June 5, 2013. [35] Nathan Bronson, Zach Amsden, George Cabrera, et al.: “TAO: Facebook’s Distributed Data Store for the Social Graph,” at USENIX Annual Technical Conference (USENIX ATC), June 2013. [36] “Apache TinkerPop3.2.3 Documentation,” tinkerpop.apache.org, October 2016. [37] “The Neo4j Manual v2
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ETL) F FacebookPresto (query engine), The divergence between OLTP databases and data warehouses React, Flux, and Redux (user interface libraries), End-to-end event streams social graphs, Graph-Like Data Models Wormhole (change data capture), Implementing change data capture fact tables, Stars and Snowflakes: Schemas for Analytics failover, Leader failure: Failover, Glossary
by Martin Kleppmann · 17 Apr 2017
as nodes or entities) and edges (also known as relationships or arcs). Many kinds of data can be modeled as a graph. Typical examples include: Social graphs Vertices are people, and edges indicate which people know each other. The web graph Vertices are web pages, and edges indicate HTML links to other
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Postgres,” blog.heroku.com, June 5, 2013. [35] Nathan Bronson, Zach Amsden, George Cabrera, et al.: “TAO: Facebook’s Dis‐ tributed Data Store for the Social Graph,” at USENIX Annual Technical Conference (USENIX ATC), June 2013. [36] “Apache TinkerPop3.2.3 Documentation,” tinkerpop.apache.org, October 2016. [37] “The Neo4j Manual v2
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Architecture transactions (see XA transactions) extract-transform-load (see ETL) F Facebook Presto (query engine), 93 React, Flux, and Redux (user interface libra‐ ries), 512 social graphs, 49 Wormhole (change data capture), 455 fact tables, 93 failover, 157, 554 (see also leader-based replication) in leaderless replication, absence of, 178 leader election
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