Tag Archives: community detection

Knitting the Dublin Clique

Last two weeks of August I spent in Dublin visiting the UCD Clique group. Even though modern technologies and Internet in particular allow us to communicate freely and efficiently, the face-to-face encountering is still irreplaceable, what I realized right after the first brainstorm session on what should I work on during my two-week visit. It turned out, that there is a significant intersection between what I would like to work on in the close future and what the guys in Dublin plan to research.

My work on cross-community dynamics relies on arbitrary community detection algorithm. However, I have not used any overlapping communities detection method. Dublin group works on two of those methods (GCE and MOSES), so I applied these and assessed preliminary the quality of the result communities. It turned out, that these communities are probably topically less cohesive, than communities mined with Infomap or Louvain methods, which, however, does not necessarily imply worse performance of Dublin methods. I would rather say, that the structure of overlapping communities is simply more “open”. I’m looking forward to¬† see, what cross-community effects we will be able to identify among these overlapping communities. I’m thinking of at least one special case not possible using non-overlapping communities: transdisciplinary, or “intermediary” communities. Those communities, which are formed by parts of other, more sharply defined communities (in terms of their topic), should themselves be identified by overlapping communities detection algorithm. Therefore, it should be easy to just look at communities, whose majority consists of other communities.

Daniel Archambauld together with Derek Greene developed very useful tool for analysis of dynamic communities: TextLUAS. It’s an application, that visualizes the dynamic life-cycle of the communities, but not only that. It also visualizes tags associated to each community, or in general, associated to sub-part of a community life-cycle. One can then very easily inspect, how topics of one community disseminate to other communities, which the community interacted with. Together with Daniel, I worked on tweaking this software for our purposes of cross-community analysis. As a result, we are now able to use it with arbitrary community detection algorithm. In future, we plan to develop a life-cycles clustering support, so that one will be able to inspect only certain type of life-cycle, e.g. “communities, which emerged from two other communities and then grew”. This will be particularly useful in case of analysis of many dynamic communities, as then the complete visualization of their life-cycles starts to be really unclear and messy.