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As a side note, the dataset is actually quite dirty: there are a lot of duplicated names and locations. Many names are listed both with and without honorifics. Many officers are listed both by first name/last name and last name/first name (and some full names also include middle names). There are numerous typos in names.
To answer Moses' question, I would suggest treating the whole network as tripartite (officer+intermediary / address / entity) and then project it to get either a network of entities or a network of officers/intermediaries.