Using Metadata to find Paul Revere - Kieran Healy. London, 1772.
I have been asked by my superiors to give a brief demonstration of the surprising effectiveness of even the simplest techniques of the new-fangled Social Networke Analysis in the pursuit of those who would seek to undermine the liberty enjoyed by His Majesty’s subjects. This is in connection with the discussion of the role of “metadata” in certain recent events and the assurances of various respectable parties that the government was merely “sifting through this so-called metadata” and that the “information acquired does not include the content of any communications”.
I will show how we can use this “metadata” to find key persons involved in terrorist groups operating within the Colonies at the present time. I shall also endeavour to show how these methods work in what might be called a relational manner. Rest assured that we only collected metadata on these people, and no actual conversations were recorded or meetings transcribed. Identifying the Pathways for Meaning Circulation using Text Network Analysis.
By Dmitry Paranyushkin, Nodus Labs.
Published October 2011, Berlin. 80legs - Custom Web Crawlers, Powerful Web Crawling, and Data Extraction. 80legs export to Gephi CSV. Spreadsheet converts tweets for social network analysis in Gephi. EDIT 05/15/13: I’ve posted two scripts, one in PHP and one in Python, that overcome the main limitation of this spreadsheet–they pull in all mentioned names rather than just the first one.
Download one or both here. If you’ve ever wanted to visualize Twitter networks but weren’t sure how to get the tweets into the right format, this spreadsheet I’ve been using in my classes might be worth a try. It prepares Twitter data for importing into Gephi, an open-source network visualization platform. Digital Middlemarch: Gephi. Formatting Dynamic Data in Gephi. Exploring Hollywood values through IMDB genres and tags. A typical Hollywood story always portraits life in a twisted way.
Movies are infused with values. Facebook friends network mapping: a Gephi tutorial. Gephi is a powerful social network modelling tool.
It also has the great advantage of simplicity: there is no need of any special skills to use it, except curiosity! This tool is used in a large number of contexts, which is why it took us to heart to propose a guide, fit to a wide audience. We offer a tutorial based on your own data from Facebook, which obviously can be used with various kinds of data! Cliquez ici pour afficher la traduction française ! Table of contents: Dominik Batorski, Analiza Sieci Społecznych. Instytut Socjologii UW, semestr I 2008/09, 60 godzin, 7 punktów ECTS.
Wykład: środa 10:00-11:45, sala 304 Warsztaty komputerowe: środa 10:00-11:45, sala 411. What is Cytoscape? Supports Many Standards Cytoscape supports a lot of standard network and annotation file formats including: SIF (Simple Interaction Format), GML, XGMML, BioPAX, PSI-MI, GraphML, KGML (KEGG XML), SBML, OBO, and Gene Association.
Delimited text files and MS Excel™ Workbook are also supported and you can import data files, such as expression profiles or GO annotations, generated by other applications or spreadsheet programs. Using this feature, you can load and save arbitrary attributes on nodes, edges, and networks. For example, input a set of custom annotation terms for your proteins, create a set of confidence values for your protein-protein interactions. Sebastien Heymann Exploratory Network Analysis with Gephi Part 1. Social Network Analysis. SPARQL Query Examples - Knowledge Wiki - Official wiki of Base22. About SPARQL is a query language that is able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.
It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. Contents Find anything with a label The following query will find all triples where subject and object are joined by rdfs:label. Find all subjects with a given object property From my contacts ontology, I find all IBMers (i.e. the hasEmployer predicate points to the individual IBM, which is an object of type Organization). Use multiple triple patterns to retrieve multiple properties From my contacts ontology, I find all IBMers and return also their email address in the query results. Datasets. 1.
Background. SPARQL Query Language for RDF. W3C Recommendation 15 January 2008 New Version Available: SPARQL 1.1 (Document Status Update, 26 March 2013)
Tutorial Layouts. This is an introduction tutorial about layouts in Gephi. It will guide you to the basic and advanced layout settings in Gephi. You will learn how to use various layouts in Gephi according to the feature you want to emphasis in the topology and the size of the network, how to avoid node overlapping and how to do some geometric transformations. Follow the Quick Start tutorial to learn the basic steps. Tutorial: Download it in PDF. Plugins requirement: The Graph Of TV Actors « Griff's Graphs. This time I wanted to see the relationship between TV actors. I’m not especially interested in TV series but I am quite interested in how they work together. The fact that many actors have been in a number of TV series creates a great network of information. Method: Humanities software, visualization and analysis.
» Demystifying Networks, Parts I & II Journal of Digital Humanities. Scott B.
Gephi Features Tour. Gephi Features Tour. SP1: Exploratory Network Analysis with Gephi. Gephi Toolkit Tutorial. #drg12 - Visualising Social Networks (Tutorial. Visualising Related Entries in Wikipedia Using Gephi. Sometime last week, @mediaczar tipped me off to a neat recipe on the wonderfully named Drunks&Lampposts blog, Graphing the history of philosophy, that uses Gephi to map an influence network in the world of philosophy. The data is based on the extraction of the “influencedBy” relationship over philosophers referred to in Wikipedia using the machine readable, structured data view of Wikipedia that is DBpedia. The recipe given hints at how to extract data from DBpedia, tidy it up and then import it into Gephi… but there is a quicker way: the Gephi Semantic Web Import plugin. (If it’s not already installed, you can install this plugin via the Tools -> Plugins menu, then look in the Available Plugin.)
Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part V. A comment from one of the Gephi developers to Getting Started With The Gephi Network Visualisation App – My Facebook Network, Part IV, in which I described how to use the Modularity statistic to partition a network in terms of several different similar subnetwork groupings, suggested that a far better way of visualising the groups was to use the Partion parameter… and how right they were… Running the Modularity statistic over my Facebook netwrok, as captured using Netvizz, and then refreshing the view in the Partition panel allows us to colour the netwrok using different partitions – such as the Modularity classes that the Modularity statistic generates and assigns nodes to: Here’s what happens when we applying the colouring: Selecting the Group view collects all the nodes in a partition together as a group: Here’s what the expanded view of one of the classes looks like, with text labels turned on:
RDF Beginner's Guide. SemanticWebImport - Gephi:Wiki. Licensing This plugin is developped inside Inria, by the Wimmics research team, with the support of the Dream team. This plugin is made available through the CeCILL-B licence. Videos The main page for the following videos can found at SemanticWebImport Plugin Videos. Description. Edelweiss:DBpediaGephi. Graphing the history of philosophy « Drunks&Lampposts.
A close up of ancient and medieval philosophy ending at Descartes and Leibniz If you are interested in this data set you might like my latest post where I use it to make book recommendations. This one came about because I was searching for a data set on horror films (don’t ask) and ended up with one describing the links between philosophers.
A New Best Friend: Gephi for Large-scale Networks. Querying DBpedia. DBpedia, as its home page tells us, "is a community effort to extract structured information from Wikipedia and to make this information available on the Web. " About. DBpedia is a crowd-sourced community effort to extract structured information from.