The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Learning Deep Representations for Graph Clustering
Open accessAbstract
Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs $k$-means algorithm on the embedding to obtain the clustering result. We show that this simple method has solid theoretical foundation, due to the similarity between autoencoder and spectral clustering in terms of what they actually optimize. Then, we demonstrate that the proposed method is more efficient and flexible than spectral clustering. First, the computational complexity of autoencoder is much lower than spectral clustering: the former can be linear to the number of nodes in a sparse graph while the latter is super quadratic due to eigenvalue decomposition. Second, when additional sparsity constraint is imposed, we can simply employ the sparse autoencoder developed in the literature of deep learning; however, it is non-straightforward to implement a sparse spectral method. The experimental results on various graph datasets show that the proposed method significantly outperforms conventional spectral clustering which clearly indicates the effectiveness of deep learning in graph clustering.
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Cites
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
UCI Machine Learning Repository
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Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Metadata
- Authors
- Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T.-Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2014-06-21
- Volume
- 28
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2405933695
- Cited by
- 657
- References
- 31
- FWCI
- 13.04
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T.-Y. (2014). Learning Deep Representations for Graph Clustering. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v28i1.8916
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