The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Open accessAbstract
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
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Cites
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Self-citationDeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 8,657
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174
Self-citationAccelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · doi:10.5555/2627435.2697068 · Cited by 2,226
Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach
Wagner, C., Dodge, S., & Alizadeh, D. (2025) · Leibniz international proceedings in informatics · doi:10.4230/lipics.giscience.2025.9 · Cited by 1,543
Anomaly Detection with Robust Deep Autoencoders
Zhou, C., & Paffenroth, R. C. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098052 · Cited by 1,436
Deep Neural Networks for Learning Graph Representations
Cao, S., Lu, W., & Xu, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10179 · Cited by 1,014
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Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Liu, L., Zhou, T., Long, G., Jiang, J., Yao, L., & Zhang, C. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/418 · Cited by 65
Self-citationPersonalized Negative Reservoir for Incremental Learning in Recommender Systems
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Metadata
- Authors
- Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., & Zhang, C.
- Venue
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
- Published
- 2019-08
- Pages
- 3670-3676
- OpenAlex
- W2964732194
- Cited by
- 573
- References
- 24
- FWCI
- 19.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
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