|Title:||Community Detection and Extraction in Networks|
|Group/Series/Folder:||Record Group 8.15 - Institute for Advanced Study|
Series 3 - Audio-visual Materials
|Notes:||HKUST International Forum on Probability and Statistics. Talk no. 17.|
Title from slide title.
The Second HKUST International Forum on Probability and Statistics (2013), held 19 December, 2013, at the Hong Kong University of Science and Technology. Co-sponsored by the HKUST Jockey Club Institute for Advanced Study and the Center for Statistical Science.
'Joint work with Yunpeng Zhao and Elizaveta Levina.'
Abstract: In this talk, the speaker establish general theory for checking consistency of community detection under the degree-corrected block model, and compare several community detection criteria under both the standard and the degree-corrected block models. The talk shows which criteria are consistent under which models and constraints, as well as compare their relative performance in practice. The speaker find that methods based on the degree-corrected block model, which includes the standard block model as a special case, are consistent under a wider class of models; and that modularity-type methods require parameter constraints for consistency, whereas likelihood-based methods do not. On the other hand, in practice the degree correction involves estimating many more parameters, and empirically find it is only worth doing if the node degrees within communities are indeed highly variable. The talk illustrates the methods on simulated networks and on a network of political blogs.
Duration: 37 min.
|Appears in Series:||8.15:3 - Audio-visual Materials|
Videos for Public -- Distinguished Lectures