Digital humanities Filtering

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

modularity analysis before filtering Run

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

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

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Results: Average Clustering Coefficient: 0.827

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

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

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

Clustering coefficient click Type 0.09

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

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

After filtering Filter

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

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

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