[statnet_help] Detecting communities

[James Moody via statnet_help]

Hi Abir -

How dynamic is your data?  i.e. are you looking at real-time sorts of dynamics (fine grained time) or waves of data collected (course grained time)?

While there are likely some avenues you could explore using latent communities within the Statnet ecosystem; I think most of the community detection tools are pre-modeling steps you'd take using other tools.

Two reasonable approaches if your data is reasonably course grained:

  1.  Cluster each wave separately.  This is the simplest-non-stupid thing to do.  Perfectly defensible as your optimizing within the wave and allows nodes maximal opportunity for change across waves.  Cost is that you have multiple communities completely independent across T, so (a) you'll have to do the work to figure out if a community at time t is the same as at t+1 (and it will never be exactly the same), and (b) there will be noise.



  1.  Convert your data to a multi-layer network, where each node identity is linked to itself across waves (*i.e. stack the edgelists, and add i_t -->  i_t+1 to the edgelist).  You'll have to do some data munging to get the IDs all sorted and such.   Then cluster the multi-layer network.  You can (should) adjust weights for clustering within/between layers - which is something of an art.   Peter Mucha's team has done a fair amount of work on this.  See https://arxiv.org/abs/0911.1824  to get you started....


PTs
Jim

James Moody
Professor of Sociology
Director, Duke Network Analysis Center



From: statnet_help <statnet_help-bounces at mailman13.u.washington.edu> On Behalf Of Abir Khazaal via statnet_help
Sent: Sunday, April 28, 2024 8:23 PM
To: statnet_help at u.washington.edu
Subject: [statnet_help] Detecting communities

Hi there,

I really hope someone can assist me.

Isn't there a package within the statnet project that I can use for detecting communities within temporal networks?

Thanks,

Abir (Abby) Khazaal | PhD candidate

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