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I would look at the following:

Betweenness centralization to determine if an elite few nodes is dominating the network more or less.  You can also look at other centralization measures.

Fragmentation coefficient is a measure of network fragility.

Triad count or simmellian ties can investigate structural changes.

If your network is directed Hub or Authority centralization may be interesting especially in citation networks.  You could also look at changes in reciprocal ties as both a percentage and total amount.

If you have a meta network (more than just people to people) there are many additional measures such as specialization, cognitive demand, etc.

Regards,

Ian McCulloh, Ph.D.
Curtin University Business School




On Tue, Sep 17, 2013 at 7:28 AM, G.F.Khan <[log in to unmask]> wrote:
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Hello Group members,

Which network parameters (apart from density, degree, and clustering coefficient) should one compare when investigating longitudinal patterns in a network (e.g., in a co-authorship network of researchers in a scientific domain captured at a fix 10 years intervals)? We are interested more to see how the co-authorship network is evolving over time e.g., is the network becoming more/less efficient and/or effective? We are interested in capturing and comparing any parameters that can shed light on the evolution of the network (e.g., using UNINET or Pajek).

One another relevant question may be what should one consider when deciding on partitioning the network data into fix intervals? Lets say we have longitudinal co-authorship data from 1970 to 2012, how should we partition this data to best capture the network evaluation?

Any suggestions are welcomed.

Thank you,


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Khan, Gohar Feroz (PhD)
Assistant Professor
Korea University of Technology & Education (KoreaTECH)
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Cheonan city, 330-708, South Korea
Office: 82-41-560-1415Mobile: +82-10-5510-8071
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