1. Digital humanities
Filtering

2. Filtering Filtering exercise
Chinese/English Buddhists study keyword co-occurrence 1987~
Compare modularity analysis before and after filtering
Giant component
Clustering coefficient
Edge weights
Frequency

3. modularity analysis before filtering
Run

4. Select

5. Results: Modularity: 0.651 Modularity with resolution: 0.651
Number of Communities: 312

6. Notice: under Node Overview
Clustering coefficient analysis
Run
Notice: under Node Overview
Not under Dynamic

7. Choose undirected

8. Results:
Average Clustering Coefficient: 0.827

9. Filter Queries 1.Frequency 2.Edge weights 3.Clustering coefficient
4. Giant component
Queries

10. Giant Component Topology Giant Component
“ Drag filter here” under Queries

11. Clustering coefficient
Attributes Range Clustering coefficient
“ Drag subfilter here” under Giant Component
Open

12. Clustering coefficient
click
Type 0.09

13. “ Drag subfilter here” under Clustering coefficient
Edge Weight
Edges edge whight
“ Drag subfilter here” under Clustering coefficient
Type in 0.6

14. Frequency Attributes Range total
“ Drag subfilter here” under edge whight
Type in 3

15. After filtering
Filter

16. modularity analysis after filtering
Results:
Modularity: 0.875
Modularity with resolution: 0.875
Number of Communities: 24

17. Also Presidential speech
Compare modularity analysis before and after filtering
Try filter insignificant terms and come up with a better modularity analysis
Use Marvel social network and aNobii book co-ownership , Movie viewing data to play with filtering, centrality, and modularity analysis

18. An example of high frequency and low clustering coefficient term “Buddhists”