The graph represents a network of 4,405 Twitter users whose tweets in the requested range contained “#pdf15 OR #wegov OR pdmteam OR @techpresident OR “personal democracy” OR Mlsif”, tweeted over the 42-day, 2-hour, 38-minute period from Saturday, 02 May 2015 at 21:24 UTC to Sunday, 14 June 2015 at 00:02 UTC.
Top 10 Vertices, Ranked by Betweenness Centrality:
Top Hashtags in Tweet in Entire Graph:
I am delighted to return to South Africa where I will participate in the Mammoth BI conference in Cape Town, on November 17-18, 2014 at the Cape Town International Conference Centre, Convention Square, 1 Lower Long Street, Cape Town, 8001, Western Cape, South Africa.
People talk about the products and services the use, love or hate all the time in social media. These conversations can be better understood through perspective of social network analysis. Network theory views the world as a web of connected people. Network analysis builds measures and visualizations of collections of connections to reveal the key people, groups and issues in these conversations. Using social media network maps and reports the confusing landscape of tweets and posts comes into focus. Information visualizations of the virtual crowds of people gathered around every brand, product, event, or service highlights the range of variation in the shape of these crowds. Six different patterns have been identified so far, allowing social media managers to recognize the nature of the brand network they have and the nature of the network they want to have. Network measures are useful as KPIs for tracking not just the size and volume of a social media stream, but also the shape and structure of the pattern of connections. The six patterns: divided, unified, fragmented, clustered, and in and out hub and spoke, are a useful guide to strategic engagement in social media.
NodeXL is sponsored by the Social Media Research Foundation and is a free and open way to get insights into connected structures like networks. NodeXL makes it easy to extract networks from many social media platforms, and automatically process the data into a network visualization and report. Using NodeXL researchers, scholars, analysts, marketing, PR and event professionals can all easily collect, store, analyze, visualize, and publish reports on social media networks.
We seek a skilled developer who can work in a distributed virtual team to maintain end extend the application.
Candidates should have the following skills:
Required: .NET development skills in C#.
Because most of the NodeXL software is written in C#.
Required: Basic system administrator skills. Amazon EC2 experience is a plus, but certainly not required.
We have three NodeXL EC2 servers that need to be kept up and running. This involves regular server monitoring, backups, Windows updates, increasing disk sizes when necessary, and so on.
Desired: Excel programming skills, preferably with Visual Studio Tools for Office (VSTO).
The NodeXL Excel Template uses VSTO. These skills might be hard to come by, though, and any C# programmer should be able to learn the Excel programming model without much difficulty.
Desired: MySQL programming and administrator skills.
The NodeXL Graph Server runs on MySQL. Bonus: Experience dealing with larger databases. The Graph Server database is approaching half a terabyte, and having someone who knows how to manage that would be a big plus.
lennyism OR insightnovation OR #IIeX Twitter NodeXL SNA Map and Report for Monday, 09 June 2014
The graph represents a network of 611 Twitter users whose tweets in the requested date range contained “lennyism OR insightnovation OR #IIeX”, or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 09 June 2014 at 00:24 UTC.
The requested date range was from Tuesday, 01 April 2014 at 00:00 UTC through Sunday, 08 June 2014 at 23:59 UTC.
The tweets in the network were tweeted over the 67-day, 4-hour, 35-minute period from Tuesday, 01 April 2014 at 00:26 UTC to Saturday, 07 June 2014 at 05:01 UTC.
There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph is directed.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
I will be running hands-on workshops on Tuesday working with NodeXL to map company and market topics. Wednesday is a basic overview of networks, social networks, social media networks. I will talk about the value of networks for businesses. The network perspective makes it easy to recognize that the same number of people can organize themselves into a variety of connected structures and that some structures are better than others for various purposes.
An example is the map of the connections among the people who tweeted about Consortium member Sage North America.