Information Technology Reference
In-Depth Information
Tabl e 8. 1 Negative binomial regression models (NBs) of Complex Network Analysis (1996-2004) at five different levels of granularity
of units of analysis
Data Source: Complex Network Analysis (1996-2004), top 100 records per time slice, 2-year sliding window
Unit of analysis
Reference
Keyword
Noun phrase
Author
Journal
Relation
Co-citation
Co-occurrence
Co-occurrence
Co-citation
Co-citation
Offset (exposure)
log 2 (Year)
log 2 (Year)
log 2 (Year)
log 2 (Year)
log 2 (Year)
Number of citing articles
3,515
3,072
3,254
3,271
3,271
Global citations
Incidence Rate Ratios ( IRRs ) in NB models
Coauthors
1 . 306
0.000
1 . 298
0.000
1 . 326
0.000
1 . 359
0 . 000
1 . 350
0 . 000
Modularity change rate
1 . 083
0 . 025
1.038
0.086
1.047
0.305
1.055
0.276
1.060
0.180
Weighted cluster linkage
3 . 160
0 . 000
0.205
0.095
1 . 33 10 8
0 . 000
2 . 879
0 . 000
1 . 204
0 . 049
Centrality divergence
0.343
0.184
3 . 679
0 . 023
1.534
0.665
23 . 400
0 . 000
7 . 620
0 . 000
Number of references
1 . 013
0 . 000
1 . 013
0 . 000
1 . 013
0 . 000
1 . 012
0 . 000
1 . 012
0 . 000
Number of pages
0 . 970
0 . 000
0 . 971
0 . 000
0 . 971
0 . 000
0 . 973
0 . 000
0 . 972
0 . 000
Dispersion parameter (™)
0.5284
0.5258
0.5150
0.5282
0.5375
2 log-likelihood
29,613
Akaike's Information Criterion (AIC) 31,787 28,347 29,508 29,522 29,629
References involves the least amount of ambiguity with the finest granularity, whereas the other four types of units introduce ambiguity
at various levels
Models constructed with units of higher ambiguity are slightly improved in terms of Akaike's Information Criterion (AIC)
31,771
28,331
29,491
29,506
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