UCINET or similar imputation of data


New Member
Dear everybody,
doing this project for explaining the differences of network attributes between twitter account. In particular I want to shows graphically and with some descriptive stats this difference, in networks where the central node is a fake account and others where it is a verified one.
To do that I downloaded a dataset of information regarding twitter account, that have been previously classified as fake or not, with 100% certainty.
Now that I want to implement them with UCINET to built graphical networks, how can I imput these?

The dataset has a .csv format, and is divided in more files. They contain two lists of ties between account and followers, and account and friends, for both verified and fake accounts.

So the data are presented in this way:

"source_id","target_id" (for followers)

"source_id","target_id" ( for friends)

Now my concern is how to work with type of data, and I thought that a way would be firstly try to built the network for the first account, let's say the number 12, where this will be the central node, but then how can I look also at the relationship between the nodes eachother?
Also I would like to let UCINET do all the work so that I don't have to write row per row every observation.

I kindly thank you for your opinion and help.