Xufei Wang, ICDM, 2010
Xufei Wang. 2010. Discovering Overlapping Groups in Social Media, the 10th IEEE International Conference on Data Mining (ICDM 2010).
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
In this paper, the concept of community is generalized to include both users and tags. Tags of a community imply the major concern of people within it.
Let denote the user set, the tay set. A community is a subset of user and tags, where k is the number of communities. As mentioned above, communities usually overlap, i.e., .On the other hand, users and their subscribed tags form a user-tag matrix M, in which each entry indicates whether user subscribes to tag . So it is reasonable to view a user as a sparse vector of tags, and each tag as a sparse vector of users.
Given notations above, the overlapping co-clustering problem can be stated formally as follows:
- A user-tag subscription matrix , where and are the numbers of users and tags.
- The number of communities k.
- k overlapping communities which consist of both users and tags.
Brief Description Of The Method
Communities that aggregate similar users and tags together can be detected by maximizing intra-cluster similarity, which is shown below: where k is the number of communities, x is the edges and c is the centroid of community. This formulation can be solved by a k-means variant.
This paper uses different methods to solve the problem of overlapping communities:
A. Synthetic Data
Synthetic data, which is controlled by various parameters, facilitates a comparative study between the uncovered and actual clusters. It has 1,000 users and 1,000 tags and with different number of clusters which range from 5 to 50.
From the experiment result, we see that correlational Learning is more effective than the other two methods in recovering overlapping clusters. It works well even when the intra-cluster link density is low. Co-clustering performs poorly because it only ﬁnds non-overlapping clusters.
B. Social Media Data
- The probability of a link between two users increases with respect to the number of tags they share.
- Correlational Learning consistently performs better, especially when the training set is small.
- Higher co-occurrence frequency suggests that two users are more similar. Similar patterns are observed in the three methods.
The author uses Co-Clustering method in Co-clustering documents and words using bipartite spectral graph partitioning as a comparison to above methods.