The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Attributed Social Network Embedding
Open accessAbstract
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Attributed Social Network Embedding framework (ASNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, ASNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, ASNE significantly outperforms node2vec with an 8.2 percent relative improvement on the link prediction task, and a 12.7 percent gain on the node classification task.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557 · cited 1×
“We conduct experiments on four public datasets, which are representative of two types of social networks—social friendship networks and academic citation networks [55].”
MethodA Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
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
ArnetMiner: extraction and mining of academic social networks
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Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models
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Accelerated Attributed Network Embedding
Huang, X., Li, J., & Hu, X. (2017) · Proceedings of the 2017 SIAM International Conference on Data Mining · doi:10.1137/1.9781611974973.71 · Cited by 311
Cited by
Heterogeneous Graph Neural Network
Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330961 · Cited by 1,591
Multi-Scale attributed node embedding
Rozemberczki, B., Allen, C., & Sarkar, R. (2021) · Journal of Complex Networks · doi:10.1093/comnet/cnab014 · Cited by 640
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
Learning Graph Embedding With Adversarial Training Methods
Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J., & Zhang, C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2932096 · Cited by 363
Metadata
- Authors
- Liao, L., He, X., Zhang, H., & Chua, T.-S.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2018-12-01
- Volume
- 30
- Issue
- 12
- Pages
- 2257-2270
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2950723285
- Cited by
- 440
- References
- 10
- FWCI
- 35.50
- Type
- journal article
- Language
- en
- Open access
- green
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
