### Table A.1. Social Accounting Matrix for Spatial Network CGE Model

in TMD DISCUSSION PAPER NO. 35 SPATIAL NETWORKS IN MULTI-REGION COMPUTABLE GENERAL EQUILIBRIUM MODELS

1999

### Table 1: Social Topology, ICT and Government Service Delivery Model Social Topology Region Network Fluidity

1980

Cited by 1

### Table 4: Network properties modeled and applicable models developed in mathematics / statistics / social network analysis and physics

2007

"... In PAGE 32: ... 5.3 Discussion Table4 provides an overview of the models that are discussed in this section and the subsequent section on modeling dynamics on networks. Table 4: Network properties modeled and applicable models developed in mathematics / statistics / social network analysis and physics ... ..."

Cited by 1

### Table 4: Network properties modeled and applicable models developed in mathematics / statistics / social network analysis and physics

"... In PAGE 32: ... 5.3 Discussion Table4 provides an overview of the models that are discussed in this section and the subsequent section on modeling dynamics on networks. Table 4: Network properties modeled and applicable models developed in mathematics / statistics / social network analysis and physics ... ..."

### Table A2.3: Social Inequality and Network Structure OLS Model Estimates

### Table 2: Comparing Dimensions in the Latent Space Social Network Model. q is the dimension of the latent space, lscriptmax is the maximized loglike- lihood from a numerical optimisation routine, and # par is the total number of parameters estimated, including the latent position coordinates. The best values of BICM and AICM are shown in bold.

in Summary

"... In PAGE 21: ... (32) Note that the values of n1 and n2 chosen do not affect model selection, because the corresponding terms cancel in computing differences between BICM values for different models, which are what matter for model comparisons. The results are shown in Table2 . In addition to our estimates of the maximized likelihood, estimates of the maximized loglikelihood by numerical optimization are shown.... In PAGE 22: ... Again it seems reasonable that the Young Turks have a more central position, suggesting that a one-dimensional latent space captures most of the main features of the data, as suggested by the relatively small differences in BICM and AICM between the one- and two-dimensional models. Our method provides standard errors for BICM and AICM, and these are also shown in Table2 . These increase rapidly, and roughly proportionally with the num- ber of parameters.... ..."

### Table 2: Comparing Dimensions in the Latent Space Social Network Model. q is the dimension of the latent space, lscriptmax is the maximized loglike- lihood from a numerical optimisation routine, and # par is the total number of parameters estimated, including the latent position coordinates. The best values of BICM and AICM are shown in bold.

in Summary

"... In PAGE 21: ... (32) Note that the values of n1 and n2 chosen do not affect model selection, because the corresponding terms cancel in computing differences between BICM values for different models, which are what matter for model comparisons. The results are shown in Table2 . In addition to our estimates of the maximized likelihood, estimates of the maximized loglikelihood by numerical optimization are shown.... In PAGE 22: ... Again it seems reasonable that the Young Turks have a more central position, suggesting that a one-dimensional latent space captures most of the main features of the data, as suggested by the relatively small differences in BICM and AICM between the one- and two-dimensional models. Our method provides standard errors for BICM and AICM, and these are also shown in Table2 . These increase rapidly, and roughly proportionally with the num- ber of parameters.... ..."

### Table 2: Comparing Dimensions in the Latent Space Social Network Model. q is the dimen- sion of the latent space, lscriptmax is the maximized loglikelihood from a numerical optimisation routine, and # par is the total number of parameters estimated, including the latent position coordinates. The best values of BICM and AICM are shown in bold.

2007

"... In PAGE 28: ...The results are shown in Table2 . In addition to our estimates of the maximized likelihood, estimates of the maximized loglikelihood by numerical optimization are shown.... In PAGE 29: ... This is why the choice is sensitive to the precise definition of the model comparison criterion. Our method provides standard errors for BICM and AICM, and these are also shown in Table2 . These increase rapidly, and roughly proportionally with the number of parameters.... ..."

Cited by 2

### Table 1. In its alignment of visualization and social network analysis indicators with the four SOC characteristics, Table 1 may be thought of as an expansion of the synthesis step (6) shown in Figure 1. Table 1. Social hypertext model for identifying sense of community in blogs

2006

"... In PAGE 7: ...3 to identify structures that can indicate sense of community on the indie music blog network from Figure 8. For each characteristic of sense of community in Table1 , we used the visualization indicator and social network analysis indicator provided by Pajek to present the structures that indicate sense of community. 4.... ..."

Cited by 5

### Table 3. Recommender systems research focused on discovering existing social networks. The left column contains modeling concepts, while the center column contains examples of implicit declarations of interest or connections mined from the systems in the right column. Notice that each system relies solely on structural, rather than semantic, information. Note also that the emtpy cell in the lower right hand corner of this matrix is a reflection that few systems take advantage of small-world properties.

"... In PAGE 12: ... We conclude by discussing small-worlds [118], a new class of social networks which present compellingopportunitiesfor serendipitousrecommendation. Table3 outlines the landscape of research showcased in this section. 4.... ..."

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