The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
struc2vec: Learning Node Representations from Structural Identity
Open accessAbstract
Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Cited by
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714
KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., & Gao, J. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271701 · Cited by 213
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Yu, W., Zheng, C., Cheng, W., Aggarwal, C. C., Song, D., Zong, B., Chen, H., & Wang, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220000 · Cited by 188
Deep Recursive Network Embedding with Regular Equivalence
Tu, K., Cui, P., Wang, X., Yu, P. S., & Zhu, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220068 · Cited by 179
Metadata
- Authors
- Ribeiro, L. F. R., Saverese, P. H. P., & Figueiredo, D. R.
- Venue
- Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2017-08-04
- Pages
- 385-394
- OpenAlex
- W2607500032
- Cited by
- 1,011
- References
- 2
- FWCI
- 51.45
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: struc2vec: Learning Node Representations from Structural Identity. https://app.cochat.ai/papers/10.1145/3097983.3098061
- The paper
- Ribeiro, L. F. R., Saverese, P. H. P., & Figueiredo, D. R. (2017). struc2vec: Learning Node Representations from Structural Identity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3097983.3098061
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
