"Extracting Communities from Networks" (Next Week at the Statistics Seminar)
Attention conservation notice: Only of interest if you are
(1) in Pittsburgh on Monday and (2) care about
discovery problem for networks, or general methods of statistical
- Ji Zhu, "Extracting Communities from Networks" (arxiv:1005.3265)
- Abstract: Analysis of networks and, in particular, discovering
communities within networks has been a focus of recent work in several fields,
with applications ranging from citation and friendship networks to food webs
and gene regulatory networks. Most of the existing community detection methods
focus on partitioning the network into cohesive communities, with the
expectation of many links between the members of the same community and few
links between different communities. However, many real-world networks
contain, in addition to communities, a number of sparsely connected nodes that
are best classified as "background". To address this problem, we propose a new
criterion for community extraction, which aims to separate tightly linked
communities from a sparsely connected background, extracting one community at a
time. The new criterion is shown to perform well in simulation studies and on
several real networks. We also establish asymptotic consistency of the
proposed method under the block model assumption.
- Joint work with Yunpeng Zhao and Liza Levina.
- Time and place: 4--5 pm on Monday, 20 September 2010, in Doherty
As always, the talk is free and open to the public.
Enigmas of Chance
Posted at September 15, 2010 20:15 | permanent link