The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings
Abstract
We present WALKLETS, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multi-scale vertex relationships in a way that is analytically derivable.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
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
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Cited by
Multi-Scale attributed node embedding
Rozemberczki, B., Allen, C., & Sarkar, R. (2021) · Journal of Complex Networks · doi:10.1093/comnet/cnab014 · Cited by 640
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Graph-Based Semi-Supervised Learning: A Comprehensive Review
Song, Z., Yang, X., Xu, Z., & King, I. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3155478 · Cited by 334
Metadata
- Authors
- Perozzi, B., Kulkarni, V., Chen, H., & Skiena, S.
- Venue
- Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
- Publisher
- ACM
- Published
- 2017-07-31
- Pages
- 258-265
- OpenAlex
- W2963410212
- Cited by
- 175
- References
- 5
- FWCI
- 8.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings. https://app.cochat.ai/papers/10.1145/3110025.3110086
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- Perozzi, B., Kulkarni, V., Chen, H., & Skiena, S. (2017). Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. https://doi.org/10.1145/3110025.3110086
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
