WebMILE: Democratizing Network Representation Learning at Scale
Summary: Democratizes network representation learning by letting user-provided embedding methods scale on large graphs via WebMILE. Docker-based, GUI, multi-level MILE/DistMILE backends run unsupervised NRL on large graphs, boosting domain researchers' productivity. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yuntian He (Ohio State University)
- 2. Yue Zhang (Ohio State University)
- 3. Saket Gurukar (Ohio State University)
- 4. Srinivasan Parthasarathy (Ohio State University)
BibTeX Citation
@article{he_vldb22,
title = {{WebMILE: Democratizing Network Representation Learning at Scale}},
author = {He, Yuntian and Zhang, Yue and Gurukar, Saket and Parthasarathy, Srinivasan},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {12},
pages = {3718--3721},
doi = {10.14778/3554821.3554883},
url = {https://doi.org/10.14778/3554821.3554883},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,915 | GraphScope: A Unified Engine For Big Graph Processing | 2021 | VLDB | 7.9666977e-05 |
| 6,962 | Demo of Marius: A System for Large-scale Graph Embeddings | 2021 | VLDB | 5.7303405e-05 |
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