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Let's say that I have a list of my friends and each of them are tagged with some of their characteristics. That is,

Ted - Jock/Outgoing/Spiritual/...
Alissa - Nerd/Introverted/Theist/...
And the list goes on.

What I aim to do is to visualize the data in a way that people with same characteristics appear closer to each other in a graph, which will finally be able to tell how many groups of people are there from the network graph that is drawn.

For example: Jock will be in a group which members are mostly outgoing and extroverted.

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    $\begingroup$ As you've described this, it isn't a network in a statistical sense. (In an everyday sense, you could call your group of friends your social network, though.) You need to decide on a distance function that captures what you mean by "closer". Then you can use multidimensional-scaling to make a plot, & clustering to detect groups. $\endgroup$
    – gung - Reinstate Monica
    Commented Feb 15, 2017 at 13:32

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Use an appropriate distance such as Jaccard or Dice, then run nonmetrical multidimensional scaling to visualize your data.

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