Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs
Summary: A production-scale Twitter recommender detects temporally correlated user-action motifs over a massive dynamic graph. Partitioned adjacency-list indexes reduce online motif detection to distributed lookups and intersections, serving millions of timely personalized recommendations. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Pankaj Gupta (Twitter)
- 2. Venu Satuluri (Twitter)
- 3. Ajeet Grewal (Twitter)
- 4. Siva Gurumurthy (Twitter)
- 5. Volodymyr Zhabiuk (Twitter)
- 6. Quannan Li (Twitter)
- 7. Jimmy Lin (Twitter)
BibTeX Citation
@article{gupta_vldb14,
title = {{Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs}},
author = {Gupta, Pankaj and Satuluri, Venu and Grewal, Ajeet and Gurumurthy, Siva and Zhabiuk, Volodymyr and Li, Quannan and Lin, Jimmy},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {13},
pages = {1379--1390},
doi = {10.14778/2733004.2733010},
url = {https://doi.org/10.14778/2733004.2733010},
year = {2014}
}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 |
|---|---|---|---|---|
| 556 | Fast Incremental and Personalized PageRank | 2011 | VLDB | 0.00016492651 |
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