Child pages
  • 5.1.4 Studying Four Major NetSci Researchers (ISI Data)

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Compare the result with Figure 5.11 and note that this network layout algorithm – and most others – are non-deterministic: different runs lead to different layouts. That said, all layouts aim to group connected nodes into spatial proximity while avoiding overlaps of unconnected or sparsely connected subnetworks.

Anchor
5.1.4.2 Author Co-Occurrence (Co-Author) Network
5.1.4.2 Author Co-Occurrence (Co-Author) Network
5.1.4.2 Author Co-Occurrence (Co-Author) Network

To produce a co-authorship network in the Sci2 Tool, select the table of all 361 unique ISI records from the 'FourNetSciResearchers' dataset in the Data Manager window. Run 'Data Preparation > Extract Co-Author Network' using the parameter:

...

The largest speed increases from the database functionality can be found in the extraction of networks. First, compare the results of a co-authorship extraction with those from section 5section #5.1.4.2 Author Co-Occurrence (Co-Author) Network. Run 'Data Preparation > Database > ISI > Extract Co-Author Network' followed by 'Analysis > Networks > Network Analysis Toolkit (NAT)'. Notice that both networks have 247 nodes and 891 edges. Visualize the extracted co-author network in GUESS using 'Visualization > Networks > GUESS' and reformat the visualization using 'Layout > GEM' and 'Layout > Bin Pack.' To apply the default co-authorship theme, go to 'Script > Run Script' and find 'yoursci2directory/scripts/GUESS/co-author-nw_database.py'. The resulting network will look like Figure 5.21.

...