The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Deep Attributed Network Embedding
Open accessAbstract
Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications. Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.
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Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
node2vec: Scalable Feature Learning for Networks
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
Inductive Representation Learning on Large Graphs
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DeepWalk: 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
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
Silva, G., Rodrigues, M., Teixeira, A., & Amorim, M. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,183
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Belkin, M., & Niyogi, P. (2002) · Advances in Neural Information Processing Systems 14 · doi:10.7551/mitpress/1120.003.0080 · Cited by 4,412
Locality Preserving Projections
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Structural Deep Network Embedding
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Cited by
Representation Learning for Attributed Multiplex Heterogeneous Network
Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., & Tang, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330964 · Cited by 465
A Comprehensive Survey on Community Detection With Deep Learning
Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., et al. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3137396 · Cited by 353
Community detection in node-attributed social networks: A survey
Chunaev, P. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100286 · Cited by 300
A Flexible Attentive Temporal Graph Networks for Anomaly Detection in Dynamic Networks
Zhu, D., Ma, Y., & Liu, Y. (2020) · 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00117 · Cited by 19
Metadata
- Authors
- Gao, H., & Huang, H.
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 3364-3370
- OpenAlex
- W2808000122
- Cited by
- 257
- References
- 23
- FWCI
- 16.23
- Type
- conference paper
- Language
- en
- Open access
- bronze
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