A model-based approach to attributed graph clustering
Graph clustering, also known as community detection, is a long-standing problem in data mining. However, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage structural and attribute information for clustering attributed graphs becomes a ne...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98766 http://hdl.handle.net/10220/12623 |
| _version_ | 1826125631456280576 |
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| author | Xu, Zhiqiang Ke, Yiping Wang, Yi Cheng, Hong Cheng, James |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Xu, Zhiqiang Ke, Yiping Wang, Yi Cheng, Hong Cheng, James |
| author_sort | Xu, Zhiqiang |
| collection | NTU |
| description | Graph clustering, also known as community detection, is a long-standing problem in data mining. However, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage structural and attribute information for clustering attributed graphs becomes a new challenge. Most existing works take a distance-based approach. They proposed various distance measures to combine structural and attribute information. In this paper, we consider an alternative view and propose a model-based approach to attributed graph clustering. We develop a Bayesian probabilistic model for attributed graphs. The model provides a principled and natural framework for capturing both structural and attribute aspects of a graph, while avoiding the artificial design of a distance measure. Clustering with the proposed model can be transformed into a probabilistic inference problem, for which we devise an efficient variational algorithm. Experimental results on large real-world datasets demonstrate that our method significantly outperforms the state-of-art distance-based attributed graph clustering method. |
| first_indexed | 2024-10-01T06:39:50Z |
| format | Conference Paper |
| id | ntu-10356/98766 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:39:50Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/987662020-05-28T07:18:13Z A model-based approach to attributed graph clustering Xu, Zhiqiang Ke, Yiping Wang, Yi Cheng, Hong Cheng, James School of Computer Engineering International Conference on Management of Data (2012) DRNTU::Engineering::Computer science and engineering Graph clustering, also known as community detection, is a long-standing problem in data mining. However, with the proliferation of rich attribute information available for objects in real-world graphs, how to leverage structural and attribute information for clustering attributed graphs becomes a new challenge. Most existing works take a distance-based approach. They proposed various distance measures to combine structural and attribute information. In this paper, we consider an alternative view and propose a model-based approach to attributed graph clustering. We develop a Bayesian probabilistic model for attributed graphs. The model provides a principled and natural framework for capturing both structural and attribute aspects of a graph, while avoiding the artificial design of a distance measure. Clustering with the proposed model can be transformed into a probabilistic inference problem, for which we devise an efficient variational algorithm. Experimental results on large real-world datasets demonstrate that our method significantly outperforms the state-of-art distance-based attributed graph clustering method. 2013-07-31T06:34:10Z 2019-12-06T19:59:28Z 2013-07-31T06:34:10Z 2019-12-06T19:59:28Z 2012 2012 Conference Paper Xu, Z., Ke, Y., Wang, Y., Cheng, H., & Cheng, J. (2012). A model-based approach to attributed graph clustering. Proceedings of the 2012 international conference on Management of Data - SIGMOD '12, 505-516. https://hdl.handle.net/10356/98766 http://hdl.handle.net/10220/12623 10.1145/2213836.2213894 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Xu, Zhiqiang Ke, Yiping Wang, Yi Cheng, Hong Cheng, James A model-based approach to attributed graph clustering |
| title | A model-based approach to attributed graph clustering |
| title_full | A model-based approach to attributed graph clustering |
| title_fullStr | A model-based approach to attributed graph clustering |
| title_full_unstemmed | A model-based approach to attributed graph clustering |
| title_short | A model-based approach to attributed graph clustering |
| title_sort | model based approach to attributed graph clustering |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98766 http://hdl.handle.net/10220/12623 |
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