Data Visualization social network
Creating a graph — NetworkX 1.7 documentation Start here to begin working with NetworkX. Create an empty graph with no nodes and no edges. >>> import networkx as nx>>> G=nx.Graph() By definition, a Graph is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc). In NetworkX, nodes can be any hashable object e.g. a text string, an image, an XML object, another Graph, a customized node object, etc. (Note: Python’s None object should not be used as a node as it determines whether optional function arguments have been assigned in many functions.)
Over the past decade there has been a growing public fascination with the complex "connectedness" of modern society. This connectedness is found in many incarnations: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior.
several dots on a map « …and then the world 2010 is already looking like it’ll be fairly busy, not least because nearly a quarter of it is gone already. Over the next twelve months, I should finish my thesis, while other projects are also being developed and carried out: I’m tutoring in a first-year unit this semester, and am currently writing up new work on the French political blog research, first outlined at IR10 last year, for both my thesis and a conference presentation. That presentation will be in June, at the International Communication Association conference in Singapore, as a paper co-authored with Lars Kirchhoff and Thomas Nicolai from Sociomantic Labs in Germany. Where my IR10 presentation looked at the text content of blog posts, this paper will be covering the links being made, in their various guises.
Using Netvizz & Gephi to Analyze a Facebook Network « persuasion This post was originally featured on http://blog.sociomantic.com, published on May 6th, 2010. Since the website will be relaunched and the post removed, I have relocated the tutorial to my personal page so that the Gephi community can continue to benefit from it. If a picture is worth a thousand words, then a graph must be worth a thousand spreadsheet rows, right? A Facebook network rendered in Gephi
Social Network Analysis
Social Network Analysis We can demonstrate the strategies for maintaining sustainable and vibrant communities, create successful communication campaigns, and show you how the framework of network analysis can be applied in social situations. Education Software
Introduction aux graphes avec Neo4j et Gephi Les solutions permettant de modéliser, stocker et parcourir de façon efficiente des graphes ont profité de plusieurs éléments qui les ont rendues populaires ces dernières années. Le premier élément aidant à leur démocratisation est l’explosion des réseaux sociaux. Un cas d’usage évident, facile à comprendre même si, étrangement, les solutions mises en œuvre ne sont pas forcément de « type graphe » (par exemple avec FlockDB chez Twitter). Le second est lié au mouvement NoSQL qui a aidé à diffuser l’idée que la base relationnelle n’est pas la seule solution de stockage et de requêtage.
I’ve been working on research-oriented digital humanities projects ever since Ruth Mostern decided to pursue a database version of Hope Wright’s An Alphabetical List of Geographical Names in Sung China in 2007. The goals have varied–sometimes the purpose was to explore data and corpora and other times the intention from the very beginning was to produce an interactive publication. But regardless of the end result, my experience of collaboration between someone who was technically more savvy (myself) and someone who was deeply embedded in their discipline (generally a tenure-track faculty, sometimes a grad student or librarian) has generated a few lessons on how collaboration in digital humanities projects can succeed, and how it may not. Digital Humanities Specialist | humanities software, visualization and analysis
Applications Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks.