Discrete Network Embedding
Open accessAbstract
Network embedding aims to seek low-dimensional vector representations for network nodes, by preserving the network structure. The network embedding is typically represented in continuous vector, which imposes formidable challenges in storage and computation costs, particularly in large-scale applications. To address the issue, this paper proposes a novel discrete network embedding (DNE) for more compact representations. In particular, DNE learns short binary codes to represent each node. The Hamming similarity between two binary embeddings is then employed to well approximate the ground-truth similarity. A novel discrete multi-class classifier is also developed to expedite classification. Moreover, we propose to jointly learn the discrete embedding and classifier within a unified framework to improve the compactness and discrimination of network embedding. Extensive experiments on node classification consistently demonstrate that DNE exhibits lower storage and computational complexity than state-of-the-art network embedding methods, while obtains competitive classification results.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Network embedding contains non-deep learning methods such as matrix factorization and random walks.
“On the other hand, network embedding contains other non-deep learning methods such as matrix factorization [33], [34] and random walks [35].”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 27 · Cited by 57
Research works printed but not linked: 4
- Qiu et al., 2018] Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and n- ode2vec. In WSDM, pages 459-467, 2018.
- Shen et al., 2017a] Fumin Shen, Yadong Mu, Yang Yang, Wei Liu, Li Liu, Jingkuan Song, and Heng Tao Shen. Clas- sification by retrieval: Binarizing data and classifiers. In SIGIR, pages 595-604, 2017.
- Tang et al., 2015a] Jian Tang, Meng Qu, and Qiaozhu Mei. Pte: Predictive text embedding through large-scale hetero- geneous text networks. In KDD, pages 1165-1174, 2015.
- Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. MGAE: marginalized graph autoencoder for graph clustering. In CIKM, pages 889-898, 2017.
- Authors
- Shen, X., Pan, S., Liu, W., Ong, Y.-S., & Sun, Q.-S.
- Affiliations
- University of Technology Sydney (AU)
- UNSW Sydney (AU)
- School of Computer and Engineering, Nanjing University of Science and Technology
- Venue
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Published
- 2018-07
- Pages
- 3549-3555
- OpenAlex
- W2808373708
- Cited by
- 57
- References
- 27
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 4 · 2022: 8 · 2021: 11 · 2020: 19 · 2019: 10 · 2018: 2
- FWCI
- 4.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Griffith Research Online (submittedVersion)
- Funders
- Nanyang Technological University
- National Natural Science Foundation of China (61,673,220, Grant No. 61673220)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- graph embedding, node classification, multi-class classifier, large-scale networks
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
