SOST71032 Social Network Analysis

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Table of Contents

Part A: Lazega Lawyers

Part A:

Visualizing and describing the co-work network with the number of nodes and the edges and densityAssignment 1

The visualization is for the increasingly important data with the social network researchers. It comes with the access for the larger amount of the data with the ability to analyse the data improved. The work needs to be refine the methods for conveying the information with the new approaches for the relational data that are emerged with the visualization on the social network analysis. The computer scientists are cognizant with the importance for the agent interaction with the social network analytics to make use of the multi-national networks with complexity that is for the number of actors and elations in between the different network size, density and the changeover the time. The approach is for the visual representation of the network with the data that tends to make use of the 2—D static displays with the spatial positioning for the nodes. It is for the communicating of information in graphs. Hence, the network complexity is mapped with the describing of the networks with the densified connections (Simpson, 2020). Hence, the advancement is the ability to collect and then analyse the social network data calls which are for handling the complexity from the changes over the time. The complexity is introduced with the network change that involves the observing changes in graphs to perceive the changes with displaying changes. The larger networks are difficult to visualize with the information that involves the dense networks that contain the larger relational amount of the data which is difficult to visualize.

For Lazega Lawyer dataset involves the methodology with the edges for the network which tends to differ from each other. The variation lower bound tends to increase the exponential forms with the network model evaluation with the goodness of fit. It is for the algorithm evaluation which comes with the Lawyer friendship network that comes with the stimulation of the model and the actual network data statistics. The simulated data model network is for the high out degree for the actual network with the upper lower quartile.

Testing for homophily for attributed practice with null model use

The normative homophily is on the selection of the advisors with the status effects. There are statical analysis to combine on the longitudinal advice network data which is collected among the judges for the normative dispositions. Considering the Lazega Lawyer, there are visualizing of the subsets for the large graph and the partitioning that involves the structure with the interactive simulation with the displayed network. Hence, visualization of the data includes the cases with the overall structures, partition, groups and the display of the relationship in between the partitions (Schecter, 2020). The simulated annealing starts with the graph positions with the node current locations that are predefined for the layout features. The generative models tend to capture the underlying and the hidden data with the mechanism that involves the network dataset. The data generation process is for the simulation and then synthesizing on the demo datasets. The study highlights about the actor based stochastic network model with the normative homophily for the selection that has been relative with the effect of status and the advice network. It is also for seeking and then sharing the advice with observing network with focusing on the stronger rules for the intra-organizational action and learning (Mellon et al, 2018).

ERGM for the statistics

The multilayer networks are for the type of relation with the exponential family random graph framework. It is for the distribution to model with the multiple layers that comes with the observed layers and the degree effects. The connections are among the actors with the binary relationship that are for the social structure and the processes. The ERGM is for the multiple layers of the network with the multi-layer exponential family random graph models. It is for the extensions that are based on the graph models with the considering on the configurations for the directed networks. Hence, there are complex social networks with the interpersonal mechanisms for the significant efforts in the community. There are multilayer approaches with the few relational layers which includes the model specifications and the node networks.

Part B: SAOM

Assess the convergence of the model

The convergence of the SAOM model highlights on the nodes and the representation of the different changes to work with the changes on the building of new state. The discussion is through Markov Chain assumptions wit including covariates from early time and the multivariate networks that comes with co-evolution models. The broader overview of the applications are for the non-directed networks with the agreements to make decisions about SAOM with network dynamics that includes the analysis for the longitudinal network data. SAOM is for online networks with growth in the political standards with the dependent variables with co-evolution for multiple networks. The greater applications for the two mode networks with the individuals come with the shared media consumption or the shared concept networks. The stochastic model is for the longitudinal data collected for the network panel design with the creation and the disappearance for the organization. The assumptions are based on the multilateral alliances with the model that is for the different probabilities of the single tie changes (Ivaniushina et al., 2019).

Modelling changes in network

The analysis is about the transitive triplets, with the multinomial choices and the node is then selected randomly with the simulation in between the observed waves. The time elapsed affect the parameters with the continuous and the discrete approach that involves the passing of the changes with the reciprocity parameters that tend to increase. The similarity homophily effect is for the larger probability with the parameter estimation with generalized model and the maximum likelihood estimation. The methods of estimator with the determining on the parameter estimates with the game theoretical model of networks. The directed networks for the SAOM and ERGM representations with the major effect that relates to the approaches for tackling the issues of dependence for panel data on the complete networks (Godechot, 2018). The communication networks are for the complex product with the intersection in between the human choice components. It comes with the theoretical network mechanisms with the framework to define on the structural efforts like the reciprocity and the transitivity. The common norms and the values are for normative stands in the uncertain situations with the interaction in between the individuals for the purposive choices. The members are likely for seeking the advice from the members with the normative similarity with the sociological and the psycho-sociological stress with the relevance for the normative similarity. Stochastic Block Model is for the Maximum Likelihood with the model parameter with the probabilistic divisions and the data clustering. The log likelihood is for he maximising of likelihood with the match of the generation of the data process of network. Hence, the product for the sum rule is for the log functions and the differentiation of the log terms. The parameters are defined with the network data preparation, pre-processing and analysis that involves the exploratory network data analysis with the use of the visualization and using the centrality measures with the eigen vector centrality (Duxbury, 2018).

Goodness of the fit model

The Principle Component Analysis tends to make use of the spectral clustering that influence the techniques to transform the data with the correlated variables. It comes with the spectral clustering and then using the techniques for exploratory data analysis. The clustering nodes in the network tends to focus on the stochastic block models with the linkage to the nodes in the block. Hence, there are probabilities to understand about the goals with the network standards that are for the centrality measures and then to define on the periphery of the nodes which are connected to the core (Bunger et al., 2020). Hence, the identifying roles are for the current restriction to the visualization based methods with discriminating the roles or the methods with the in-degree and the our-degree processes. It comes with the performance of formal regression which comes through scalar measures and the pattern of connectivity. The dependency is based on the analysis for the non-punitive concurring decisions with the focus on the relative sectors. Hence, the business models are defined for the component with the potentially driven networks. Hence, the model specification are hypothesized with driving network evolution and then suspecting for driving the dynamics of the network as well.

References

Bunger, A. C., & Nooraie, R. Y. (2020). Social network analysis. In Handbook on Implementation Science. Edward Elgar Publishing.

Duxbury, S. W. (2018). Diagnosing multicollinearity in exponential random graph models. Sociological Methods & Research, 0049124118782543.

Godechot, O. (2018). Networks for Economic Sociology (and Not the Other Way Around).

Ivaniushina, V., Titkova, V., & Alexandrov, D. (2019). Peer influence in adolescent drinking behaviour: a protocol for systematic review and meta-analysis of stochastic actor-based modeling studies. BMJ open, 9(7), e028709.

Mellon, J., & Evans, D. (2018). Forecasting social network reaction to disruption: current practices and new directions. Available at SSRN 3144118.

Poquet, O., Tupikina, L., & Santolini, M. (2020, March). Are forum networks social networks? A methodological perspective. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 366-375).

Schecter, A., & Contractor, N. (2017). Understanding and assessing collaborative processes through relational events. In Innovative Assessment of Collaboration (pp. 223-231). Springer, Cham.

Simpson, C. R. (2020). Farm size shapes friend choice amongst rice producers in China: Some evidence for the theory of network ecology. Social Networks, 61, 107-127.

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