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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sachan, Mrinmaya Hovy, Eduard Xing, Eric P. Dubey, Avinava Srivastava, Shashank |
| Abstract | In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network. Assuming the social network to be an integer-weighted graph (where the weights can be intuitively defined as the number of common friends, followers, documents exchanged, etc.), we transform the social network to a more efficient representation. In this new representation, each user is a bag of her one-hop neighbors. We propose a mixed-membership model to identify compact communities using this transformation. Next, we augment the representation and the model to incorporate user-content information imposing topical consistency in the communities. In our model a user can belong to multiple communities and a community can participate in multiple topics. This allows us to discover community memberships as well as community and user interests. Our method outperforms other well known baselines on two real-world social networks. Finally, we also provide a fast, parallel approximation of the same. |
| Starting Page | 503 |
| Ending Page | 512 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450323512 |
| DOI | 10.1145/2556195.2556219 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-02-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Graphical models Community detection Social networks |
| Content Type | Text |
| Resource Type | Article |
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