Multi-view graph clustering
This study explores the adaptation and application of Deep Modularity Networks (DMoN) for multi-view graph clustering, a technique crucial for extracting insights from complex networks across various domains. By integrating multiple perspectives or views of graph data, our approach, termed Multi-vi...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175282 |
| _version_ | 1811677792757088256 |
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| author | Yap, Nicholas Guo Dong |
| author2 | Ke Yiping, Kelly |
| author_facet | Ke Yiping, Kelly Yap, Nicholas Guo Dong |
| author_sort | Yap, Nicholas Guo Dong |
| collection | NTU |
| description | This study explores the adaptation and application of Deep Modularity Networks (DMoN) for multi-view graph clustering, a technique crucial for extracting insights from complex networks across various
domains. By integrating multiple perspectives or views of graph data, our approach, termed Multi-view Deep Modularity Networks (MVDMoN), seeks to enhance clustering performance beyond what is
achievable with single-view analyses. We focus on the UCI Multiple Features dataset, leveraging its six
distinct views of handwritten digits to test our model’s efficacy. Our results indicate that MVDMoN can
effectively adjust the weightage of various views to optimize clustering outcomes, revealing its adaptability
and potential for uncovering valuable patterns not evident when views are analyzed in isolation. The
study also enhances clustering performance over its single-view counterpart, particularly in terms of
Normalized Mutual Information (NMI) and F1 score. Through potential enhancements such as ablation
studies and exploration of additional datasets, subsequent research can build upon our findings, advancing
the field towards more nuanced and effective clustering solutions. |
| first_indexed | 2024-10-01T02:43:00Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175282 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:43:00Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752822024-04-26T15:43:31Z Multi-view graph clustering Yap, Nicholas Guo Dong Ke Yiping, Kelly School of Computer Science and Engineering ypke@ntu.edu.sg Computer and Information Science This study explores the adaptation and application of Deep Modularity Networks (DMoN) for multi-view graph clustering, a technique crucial for extracting insights from complex networks across various domains. By integrating multiple perspectives or views of graph data, our approach, termed Multi-view Deep Modularity Networks (MVDMoN), seeks to enhance clustering performance beyond what is achievable with single-view analyses. We focus on the UCI Multiple Features dataset, leveraging its six distinct views of handwritten digits to test our model’s efficacy. Our results indicate that MVDMoN can effectively adjust the weightage of various views to optimize clustering outcomes, revealing its adaptability and potential for uncovering valuable patterns not evident when views are analyzed in isolation. The study also enhances clustering performance over its single-view counterpart, particularly in terms of Normalized Mutual Information (NMI) and F1 score. Through potential enhancements such as ablation studies and exploration of additional datasets, subsequent research can build upon our findings, advancing the field towards more nuanced and effective clustering solutions. Bachelor's degree 2024-04-22T08:13:57Z 2024-04-22T08:13:57Z 2024 Final Year Project (FYP) Yap, N. G. D. (2024). Multi-view graph clustering. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175282 https://hdl.handle.net/10356/175282 en SCSE23-0403 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Yap, Nicholas Guo Dong Multi-view graph clustering |
| title | Multi-view graph clustering |
| title_full | Multi-view graph clustering |
| title_fullStr | Multi-view graph clustering |
| title_full_unstemmed | Multi-view graph clustering |
| title_short | Multi-view graph clustering |
| title_sort | multi view graph clustering |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/175282 |
| work_keys_str_mv | AT yapnicholasguodong multiviewgraphclustering |