I ask you to do exploratory network analysis of Padgett & Ansell...

I ask you to do exploratory network analysis of Padgett & Ansell dataset on the relationships between 92 elite Florentine families.
Please submit (1) pdf OR rtf OR doc files with the report and (2) the respective script in R OR txt
To do the final assignment you need

1. to read the original paper and formulate hypotheses (John F. Padgett & Christopher K. Ansell (1994) Robust Action and the Rise of the Medici, 1400-1434, in: The American Journal of Sociology, Vol. 98, No. 6. (May, 1993), pp. 1259-1319).
2. to construct the attributes necessarily to check the hypotheses (the attributes are available on с. 1312. (Table B1)).
3. to perform the exploratory network analysis, present measures and visualizations, give interpretations to the measures in your paper. The dataset with the linkages in csv-files is attached to letter (the original archive from Prof. Padgett with matrices in text files in DAT is also attached); the types of the linkages are described on p. 1276 (Fig. 2a) & 1277 (Fig. 2b) of the paper; the plan for analysis will be given below.

The goal of your final individual report is to demonstrate that personally you are familiar with the key network measures, can apply them to a specific dataset, can give interpretations to the measures and can use the measures and algorithms to formulate network-related hypothesis. Please note that the report is an individual work and should meet all the standards required by Antiplagiat system (i.e. do you research personally and cite properly).

Basic plan for the report is given below.
(0) Description of your data (networks and attributes; the reasoning behind construction of the certain attributes) and hypotheses.
Demonstration of your work with the following groups of measures:
(1) Centrality (Degree, Closeness, Betweenness, Eigenvector) & Centralization measures.
(2) Cohesion measures, including (1) Density, (2) Reciprocity, (3) Transitivity, (4) Cyclicality, (5) Homophily for categorical and continuous properties and degree (assortativity)
(3) Algorithms for community detection, including: (1) One algorithm for overlapping communities (2) One algorithm based on modularity maximization (3) One algorithm based on dynamics
(4) Ego-network measures, (1) Burt’s measures related to structural holes, (2) Alter composition & Ego-alter similarity measures, (3) Gould and Fernandez brokerage roles, (4) for bonus scores: ego-network densities or node level transitivity
(5) Blockmodelling (algorithms for Structural & Regular equivalence, and Optimization)