Graph-Based Semi-Supervised Local Clustering with Few Labeled Nodes
Abstract
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.
Cite
Text
Shen et al. "Graph-Based Semi-Supervised Local Clustering with Few Labeled Nodes." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/466Markdown
[Shen et al. "Graph-Based Semi-Supervised Local Clustering with Few Labeled Nodes." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/shen2023ijcai-graph/) doi:10.24963/IJCAI.2023/466BibTeX
@inproceedings{shen2023ijcai-graph,
title = {{Graph-Based Semi-Supervised Local Clustering with Few Labeled Nodes}},
author = {Shen, Zhaiming and Lai, Ming-Jun and Li, Sheng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {4190-4198},
doi = {10.24963/IJCAI.2023/466},
url = {https://mlanthology.org/ijcai/2023/shen2023ijcai-graph/}
}