ILPnet2 social network analysis

1. ILPnet2 social network analysis Miha Grčar Course in Knowledge Management Lecturer: prof. dr. Nada Lavrac Ljubljana, January 2007

2. Outline of the presentation • Data preprocessing • Directing the network • Social vs. structural prestige • Correlation between the two • Triad census of strong components in the co-authorship network • Hierarchy of authors with respect to co-authorship • Conclusions Miha Grčar

3. Data preprocessing # citations # (joint) publications Miha Grčar

4. Data preprocessing Pajek network file SQL Miha Grčar

5. Miha Grčar

6. Directing the network • Create a complete directed network • Logarithmize and normalize values • Allow each author to keep at most k outgoing arcs – the ones with the highest weights • Calculate proximity prestige for several different values of k and a, and determine its correlation with/to the social prestige represented by the number of citations Miha Grčar

7. Correlation Miha Grčar

8. Strong components triad census for k=3, a=1 ------------------------------------------------------------------------------------------------------ Type Number of triads (ni) Expected (ei) (ni-ei)/ei Model ------------------------------------------------------------------------------------------------------ 3 - 102 0 61.84 -1.00 Balance 16 - 300 0 0.00 -1.00 ------------------------------------------------------------------------------------------------------ 1 - 003 2985835 2984491.39 0.00 Clusterability ------------------------------------------------------------------------------------------------------ 4 - 021D 10 61.84 -0.84 Ranked Clusters 5 - 021U 1534 61.84 23.80 9 - 030T 28 0.33 85.14 12 - 120D 0 0.00 -1.00 13 - 120U 0 0.00 -1.00 ------------------------------------------------------------------------------------------------------ 2 - 012 44402 47062.30 -0.06 Transitivity ------------------------------------------------------------------------------------------------------ 14 - 120C 0 0.00 -1.00 Hierarchical Clusters 15 - 210 0 0.00 -1.00 ------------------------------------------------------------------------------------------------------ 6 - 021C 55 123.69 -0.56 Forbidden 7 - 111D 0 0.33 -1.00 8 - 111U 0 0.33 -1.00 10 - 030C 0 0.11 -1.00 11 - 201 0 0.00 -1.00 ------------------------------------------------------------------------------------------------------ Chi-Square: 37695.2629*** 10 cells (62.50%) have expected frequencies less than 5. The minimum expected cell frequency is 0.00. Miha Grčar

9. Strong components in k=3, a=1 Miha Grčar

10. Strong components, hierarchical view Miha Grčar

11. People, ranked clusters 1. Remove inter-cluster arcs 2. Convert bidirected intra-cluster arcs into edges 3. Remove all remaining arcs Miha Grčar

12. People, hierarchical view Miha Grčar

13. Conclusions • (Typical) data-mining data preprocessing process was presented • We have shown that some directed network models reflect the ranking of authors according to the citations quite well • We showed Pajek can be used to explore rankings and hierarchies in social networks • Slovene ILP team rocks!  Miha Grčar