The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Multi-Scale attributed node embedding
Open accessAbstract
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighbourhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE). Capturing attribute-neighbourhood relationships over multiple scales is useful for a range of applications, including latent feature identification across disconnected networks with similar features. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are computationally efficient and outperform comparable models on social networks and web graphs.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
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
(2010) · International Conference on Machine Learning · Cited by 13,995
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 Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
Karypis, G., & Kumar, V. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827595287997 · Cited by 5,762
Neural Word Embedding as Implicit Matrix Factorization
Levy, O., & Goldberg, Y. (2014) · neural information processing systems · Cited by 1,582
GraRep: Learning Graph Representations with Global Structural Information
Cao, S., Lu, W., & Xu, Q. (2015) · Proceedings of the 24th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2806416.2806512 · Cited by 1,571
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., & Hsieh, C.-J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330925 · Cited by 1,331
Asymmetric Transitivity Preserving Graph Embedding
Ou, M., Cui, P., Pei, J., Zhang, Z., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939751 · Cited by 1,248
Fast Graph Representation Learning with PyTorch Geometric
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 1,132
struc2vec: Learning Node Representations from Structural Identity
Ribeiro, L. F. R., Saverese, P. H. P., & Figueiredo, D. R. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098061 · Cited by 1,011
Network representation learning with rich text information
Yang, C. H., Liu, Z., Zhao, D., Sun, M., & Chang, E. Y. (2015) · Cited by 977
Getoor, L. (2006) · Advanced Information and Knowledge Processing · doi:10.1007/1-84628-284-5_7 · Cited by 612
Predict then Propagate: Graph Neural Networks meet Personalized PageRankPreprint
Klicpera, J., Bojchevski, A., & Günnemann, S. (2018) · arXiv (Cornell University) · Cited by 526
Attributed Social Network Embedding
Liao, L., He, X., Zhang, H., & Chua, T.-S. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2819980 · Cited by 440
Scaling Graph Neural Networks with Approximate PageRank
Bojchevski, A., Gasteiger, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M., & Günnemann, S. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403296 · Cited by 371
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
Query-driven active surveying for collective classification
Namata, G. M. S., London, B., Getoor, L. C., & Huang, B. (2012) · Mining and Learning with Graphs · Cited by 276
GEMSEC: graph embedding with self clustering
Rozemberczki, B., Davies, R., Sarkar, R., & Sutton, C. (2019) · Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining · doi:10.1145/3341161.3342890 · Cited by 272
Self-citationIt's who you know: graph mining using recursive structural features
Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020512 · Cited by 223
Watch Your Step: Learning Node Embeddings via Graph AttentionPreprint
Abu-El-Haija, S., Perozzi, B., Al‐Rfou, R., & Alemi, A. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09599 · Cited by 181
Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings
Perozzi, B., Kulkarni, V., Chen, H., & Skiena, S. (2017) · Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 · doi:10.1145/3110025.3110086 · Cited by 175
Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs
Rózemberczki, B., Kiss, O., & Sarkar, R. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412757 · Cited by 154
Self-citationBinarized attributed network embedding
Yang, H., Pan, S., Zhang, P., Chen, L., Lian, D., & Zhang, C. (2018) · 2018 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2018.8626170 · Cited by 102
Learning Role-based Graph EmbeddingsPreprint
Ahmed, N. K., Rossi, R. A., Lee, J. B., Willke, T. L., Zhou, R., Kong, X., & Eldardiry, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02896 · Cited by 76
Fast Sequence-Based Embedding with Diffusion Graphs
Rózemberczki, B., & Sarkar, R. (2018) · Springer proceedings in complexity · doi:10.1007/978-3-319-73198-8_9 · Cited by 65
Self-citationSINE: Scalable Incomplete Network Embedding
Zhang, D., Yin, J., Xingquan Zhu, A., & Chengqi Zhang, B. (2018) · Proceedings · doi:10.1109/icdm.2018.00089 · Cited by 37
Enhanced Network Embedding with Text Information
Yang, S., & Yang, B. X. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545577 · Cited by 29
What the Vec? Towards Probabilistically Grounded EmbeddingsPreprint
Allen, C., Balažević, I., & Hospedales, T. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12164 · Cited by 19
Self-citationChen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
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Geom-GCN: Geometric Graph Convolutional Networks
Pei, H., Wei, B., Chang, K. C., Yu, L., & Yang, B. X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05287 · Cited by 396
Metadata
- Authors
- Rozemberczki, B., Allen, C., & Sarkar, R.
- Venue
- Journal of Complex Networks
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-04-01
- Volume
- 9
- Issue
- 2
- ISSN
- 2051-1310 (ISSN-L)
- 2051-1329
- OpenAlex
- W2975966238
- Cited by
- 640
- References
- 31
- FWCI
- 33.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
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