Difference between revisions of "Xufei Wang, ICDM, 2010"
From Cohen Courses
Jump to navigationJump to search| Line 13: | Line 13: | ||
== Summary == | == Summary == | ||
In this [[Category::paper]], the authors propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. The basic ideas are: | In this [[Category::paper]], the authors propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. The basic ideas are: | ||
| + | |||
| + | * To discover overlapping communities in social media. Diverse interests and interactions that human beings can have in online social life suggest that one person often belongs more than one community. | ||
| + | |||
| + | * To use user-tag subscription information instead of user-user links. Metadata such as tags become an important source | ||
| + | in measuring the user-user similarity. The paper shows that more accurate community structures can be obtained by scrutinizing tag information. | ||
| + | |||
| + | * To obtain clusters containing users and tags simultaneously. Existing co-clustering methods cluster users/tags separately. Thus, it is not clear which user cluster corresponds to which tag cluster. But the proposed method is able to find out user/tag group structure and their correspondence | ||
== Brief description of the method == | == Brief description of the method == | ||
Revision as of 23:02, 27 March 2011
Contents
Citation
Xufei Wang. 2010. Discovering Overlapping Groups in Social Media, the 10th IEEE International Conference on Data Mining (ICDM 2010).
Online Version
http://dmml.asu.edu/users/xufei/Papers/ICDM2010.pdf
Databases
Summary
In this paper, the authors propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. The basic ideas are:
- To discover overlapping communities in social media. Diverse interests and interactions that human beings can have in online social life suggest that one person often belongs more than one community.
- To use user-tag subscription information instead of user-user links. Metadata such as tags become an important source
in measuring the user-user similarity. The paper shows that more accurate community structures can be obtained by scrutinizing tag information.
- To obtain clusters containing users and tags simultaneously. Existing co-clustering methods cluster users/tags separately. Thus, it is not clear which user cluster corresponds to which tag cluster. But the proposed method is able to find out user/tag group structure and their correspondence
