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Robert Hanneman's Homepage

Robert Hanneman's Homepage
Robert Hanneman and Mark Riddle. 2005. Introduction to social network methods. (free introductory textbook on social network analysis). Read on-line as .html or download a .pdf version or download a .epub version (thanks to Q. Ethan McCAllum) or download a .mobi version (thanks to Q. Ethan McCallum) Catalan language translation of the introduction by David Leoney Polish language translation of the introduction by AndreyFomin is available at: Robert Hanneman. 1988. Robert Hanneman. 2005. Izquierdo, L.R. and Hanneman, R.A. 2006.Introduction to the Formal Analysis of Social Networks Using Mathematica. .

http://faculty.ucr.edu/~hanneman/

Related:  Social Network Analysis

Introduction to Network Visualization with GEPHI New tutorial available! A completely new version of this tutorial has been published, with 2 complete and complementary datasets to learn and explore many basic and advanced features of Gephi: To the new tutorial Gephi workshop at University of Bern (photo Radu Suciu) Projects - *ORA Overview | People | Sponsors | Publications | Hardware Requirements | Software | Training & Sample Data *ORA is a dynamic meta-network assessment and analysis tool developed by CASOS at Carnegie Mellon. It contains hundreds of social network, dynamic network metrics, trail metrics, procedures for grouping nodes, identifying local patterns, comparing and contrasting networks, groups, and individuals from a dynamic meta-network perspective. *ORA has been used to examine how networks change through space and time, contains procedures for moving back and forth between trail data (e.g. who was where when) and network data (who is connected to whom, who is connected to where …), and has a variety of geo-spatial network metrics, and change detection techniques. *ORA can handle multi-mode, multi-plex, multi-level networks. It can identify key players, groups and vulnerabilities, model network changes over time, and perform COA analysis.

The original proposal of the WWW, HTMLized A hand conversion to HTML of the original MacWord (or Word for Mac?) document written in March 1989 and later redistributed unchanged apart from the date added in May 1990. Provided for historical interest only. Sources And Methods: The Potential of Social Network Analysis in Intelligence (In case you missed our most recent article over at e-International Relations or at OODALoop, we are reprinting it here!)The legality of the National Security Agency’s (NSA’s) use of US citizens’ metadata to identify and track foreign intelligence organizations and their operatives is currently a subject of much debate. Less well understood (and consequently routinely misreported) are the capabilities and limitations of social network analysis, the methodology often used to evaluate this metadata.

OnDemand - Open Source Collaborative Networking for Intranets an A collaborative help system that finally gives your customers and agents the knowledge they need in real time MindTouch® is a cloud based self-service help center and a knowledge-as-a-service platform that prevents support requests and improves your existing customer support systems. For the first time, you can update and deliver product knowledge in real-time, everywhere and across all channels minimizing support requests. Track your customer behavior with web analytics and MindTouch content analytics to improve your product help content, product strategy, customer success programs and customer retention while simultaneously lowering your support costs. Speed – Deploy faster. Spot trends faster.

Step by Step Social Network Analysis using Gephi: Getting Started In continuation to my previous blog post on Social Network Analysis using Gephi, I’m writing this post to explain how do create a very simple social network analysis using Gephi. You can also look at a very good introduction to Gephi written by Martin Grandjean here Goal and Scenario: We have a friends network we want to depict visually how the friends are interconnected with each other. The goal is to understand how to use Gephi Step by step along with having very fundamental understanding of how the data is represented.

Derrick de Kerckhove Derrick de Kerckhove (born 1944) is the author of The Skin of Culture and Connected Intelligence and Professor in the Department of French at the University of Toronto, Canada. He was the Director of the McLuhan Program in Culture and Technology from 1983 until 2008. In January 2007, he returned to Italy for the project and Fellowship “Rientro dei cervelli”, in the Faculty of Sociology at the University of Naples Federico II where he teaches "Sociologia della cultura digitale" and "Marketing e nuovi media". He was invited to return to the Library of Congress for another engagement in the Spring of 2008.[1] He is research supervisor for the PhD Planetary Collegium M-node[2] directed by Francesco Monico.

OECD – Your Better Life Index Average personal index for Germany, men, 15–24 How’s life? There is more to life than the cold numbers of GDP and economic statistics – This Index allows you to compare well-being across countries, based on 11 topics the OECD has identified as essential, in the areas of material living conditions and quality of life. Download executive summary Download the index data Learn more about the index Better Life BlogArchive

Linked Data An introductory overview of Linked Open Data in the context of cultural institutions. In computing, linked data (often capitalized as Linked Data) describes a method of publishing structured data so that it can be interlinked and become more useful. It builds upon standard Web technologies such as HTTP, RDF and URIs, but rather than using them to serve web pages for human readers, it extends them to share information in a way that can be read automatically by computers. This enables data from different sources to be connected and queried.[1] Tim Berners-Lee, director of the World Wide Web Consortium, coined the term in a design note discussing issues around the Semantic Web project.[2] Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities With urban population increasing dramatically worldwide, cities are playing an increasingly critical role in human societies and the sustainability of the planet. An obstacle to effective policy is the lack of meaningful urban metrics based on a quantitative understanding of cities. Typically, linear per capita indicators are used to characterize and rank cities. However, these implicitly ignore the fundamental role of nonlinear agglomeration integral to the life history of cities. As such, per capita indicators conflate general nonlinear effects, common to all cities, with local dynamics, specific to each city, failing to provide direct measures of the impact of local events and policy.

FOAF (software) FOAF logo. FOAF is a descriptive vocabulary expressed using the Resource Description Framework (RDF) and the Web Ontology Language (OWL). Computers may use these FOAF profiles to find, for example, all people living in Europe, or to list all people both you and a friend of yours know.[1][2] This is accomplished by defining relationships between people. Each profile has a unique identifier (such as the person's e-mail addresses, a Jabber ID, or a URI of the homepage or weblog of the person), which is used when defining these relationships. Tim Berners-Lee, in a 2007 essay,[3] redefined the Semantic web concept into the Giant Global Graph, where relationships transcend networks and documents.

Measurefest: network mapping and visualising relative influence - Brilliant Noise Last week I spoke at Measurefest, a conference dedicated to analytics, marketing measurement and CRO. The topic of my talk was, “Network mapping and visualising relative influence”. In case you missed it, here it is in blog form… We need to move on from basic influencer identification based on Twitter bios, to finding people based on their network connections.Drawing networks on NodeXL can visually communicate the meaning of relevance in influencer identification to senior stakeholders.And we can use conversational data from influencer networks to inform and evaluate content strategy.

Introduction to social network methods by bihonglee Aug 25

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