A searchable and interactive network graph of Data Scientists archived in February 2016
The Twitter Network of 30,000 “Data Scientists” and their connections
The visualisation was created using Gephi after applying 3 steps on NodeXL. Step 1 – A Twitter search for the term ‘datascientist’ on 02/02/2016 that returned… Step 2 – 1,132 user accounts Step 3 – Input the 1,132 user accounts in to a Twitter User search that returned 30,000 user accounts. Export into Gephi with Modularity Class selected to differentiate groups and OpenOrd to set the graph.
You can search by name or select various groups that have been classed together according to modularity.
The Forbes 2015’s Billionaires (2015). Forbes ranks more than 1,800 billionaires and these are their company, and affilations with Government Bodies etc – This list was then adapted by LittleSis http://littlesis.org/lists/846-the-world-s-billionaires-2015/members . I have then used it to construct a network which I’ve then visualised using Gephi.
Clicking on the image opens an interactive and searchable version.
Forbes magazine has been publishing the list of The World’s Most Powerful People since 2009. The number of people in the list is proportional to the global population with the ratio being one slot for every 100 million people on Earth. When the list started in 2009, there were 67 people on the list and the latest list from year 2014 had 72 people. According for Forbes, the list is calculated based on the person’s influence over lots of other people (e.g. Pope Francis, Wal-Mart CEO, Doug McMillon), financial resources controlled by the people (e.g. GDP, market capitalization, profits, assets, revenues and net worth), power in multiple spheres (e.g. Bill Gates), active use of power by the people (e.g. Vladimir Putin). While this list gives a snapshot of global ranking, it does not reveal information about past and present network connections and inter-linkages between people and organisations, the spread of power across the network, information about key entities who act as network intermediaries for power and which cluster of entities are most prominent in this global power structure. This is where we complement the Forbes data with LittleSis, which is a database of who-knows-who at the heights of business and government. LittleSis has information of the global rich and powerful such as past and present organizational affiliations (employment, directorships, memberships, alumni networks), donations (political contributions, grants), social connections (family ties, mentorships, friendships), professional connections (partnerships, supervisory relationships), services/contracts (legal representation, government contracts, lobbying services) etc. http://www.datasciencecentral.com/profiles/blogs/the-alternative-list-of-global-power-elite
For further information regarding this interactive and searchable visualisation please contact email@example.com or @soci
While I work out how to extract data from Wikipedia thought I should post this here.
The bigger the node, the larger the betweenness centrality score i.e. the bigger influence that person had on the rest of the network. These are the most influential figures in the network. However I do agree with…
This however brings us to one of the largest problems in doing work like this; the graph is intrinsically “wrong”. Brendan Griffen.
The inspiration for this graph was from Griffen – who had promised to make the interactive version of his graph available but is yet to do so.
To show just how “wrong” these graphs are – here is exactly the same data but this time a different algorithm (hub) has been used to size the nodes…
The map is only useful when you zoom in close to particular areas or type in a name in the search function.
Below is a close up of a red section that on closer inspection is a group of comedians…
Interactive visualisation of the Tweets that contained the hashtag #bigdata July 2015. As it contains approximately 50,000 nodes and 130,000 edges it takes a little bit of time to load. Another hashtag closely linked to #bigdata is #clickbait. This may … Continue reading →