Graph-based Semi-supervised Local Clustering with Few Labeled Nodes
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916359958429696 |
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| author | Shen, Zhaiming Lai, Ming-Jun Li, Sheng |
| author_facet | Shen, Zhaiming Lai, Ming-Jun Li, Sheng |
| contents | Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. Our approach improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of the existing works, which is the low quality of initial cut. Extensive experimental results on various datasets demonstrate the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_11114 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Graph-based Semi-supervised Local Clustering with Few Labeled Nodes Shen, Zhaiming Lai, Ming-Jun Li, Sheng Machine Learning Numerical Analysis Local clustering aims at extracting a local structure inside a graph without the necessity of knowing the entire graph structure. As the local structure is usually small in size compared to the entire graph, one can think of it as a compressive sensing problem where the indices of target cluster can be thought as a sparse solution to a linear system. In this paper, we apply this idea based on two pioneering works under the same framework and propose a new semi-supervised local clustering approach using only few labeled nodes. Our approach improves the existing works by making the initial cut to be the entire graph and hence overcomes a major limitation of the existing works, which is the low quality of initial cut. Extensive experimental results on various datasets demonstrate the effectiveness of our approach. |
| title | Graph-based Semi-supervised Local Clustering with Few Labeled Nodes |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2211.11114 |