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Self-Supervised Graph Convolutional Network for Multi-View Clustering | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. Existing graph convolutional network (GCN) based multi-view learning methods directly use the graph structure as view descriptor, which may inhibit the ability of multi-view learning for multimedia data.
2. This paper proposes an end-to-end self-supervised graph convolutional network for multi-view clustering (SGCMC) to construct a new view descriptor for graph-structured data by mapping the raw node content into the complex space via Euler transformation.
3. Extensive experimental results indicate that the proposed SGCMC outperforms the state-of-the-art methods.
Article analysis:
The article is written in a clear and concise manner, making it easy to understand and follow. The authors provide a comprehensive overview of existing GCN based multi-view learning methods and their shortcomings, and then present their proposed solution to address these issues. The article also provides extensive experimental results to support their claims, demonstrating that their proposed method outperforms existing approaches.
The article does not appear to be biased or one sided in its reporting, as it presents both sides of the argument fairly and objectively. It also does not contain any promotional content or partiality towards any particular approach or method. Furthermore, all claims made are supported with evidence from experiments conducted by the authors, making them reliable and trustworthy.
The only potential issue with this article is that it does not explore any counterarguments or alternative solutions to the problem presented in detail. While this is understandable given the scope of this paper, it would have been beneficial if some counterarguments were explored in order to provide a more comprehensive overview of possible solutions to this problem. Additionally, while possible risks are noted in passing, they are not discussed in depth which could have provided further insight into potential drawbacks of using this approach.