MOSER: Scalable Network Motif Discovery using Serial Test
Summary: MOSER applies the serial test to provide statistical guarantees on motif sample quality rather than heuristic sampling. With two incremental subgraph-counting algorithms it scales NMD dramatically (up to 5 orders of magnitude) and improves downstream tasks like link prediction. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Mohammad Matin Najafi (University of Hong Kong)
- 2. Chenhao Ma (Chinese University of Hong Kong)
- 3. Xiaodong Li (University of Hong Kong)
- 4. Reynold Cheng (University of Hong Kong)
- 5. Laks V.S. Lakshmanan (University of British Columbia)
BibTeX Citation
@article{najafi_vldb24,
title = {{MOSER: Scalable Network Motif Discovery using Serial Test}},
author = {Najafi, Mohammad Matin and Ma, Chenhao and Li, Xiaodong and Cheng, Reynold and Lakshmanan, Laks V.S.},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {591--603},
doi = {10.14778/3632093.3632118},
url = {https://doi.org/10.14778/3632093.3632118},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,226 | ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model | 2024 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 189 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00026114928 |
| 1,378 | Effective Community Search over Large Spatial Graphs | 2017 | VLDB | 0.00010971508 |
| 11,690 | On Analyzing Graphs with Motif-Paths | 2021 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,423 | Memory-Aware Framework for Efficient Second-Order Random Walk on Large Graphs | 2020 | SIGMOD |
| 2 | 8,206 | Mining Top-k Pairs of Correlated Subgraphs in a Large Network | 2020 | VLDB |
| 3 | 10,546 | TIMEST: Temporal Information Motif Estimator Using Sampling Trees | 2026 | VLDB |
| 4 | 10,927 | MOMENTI: Scalable Motif Mining in Multidimensional Time Series | 2025 | VLDB |
| 5 | 10,802 | Most Similar Biclique Search at Scale | 2025 | VLDB |
| 6 | 1,944 | Real-Time Twitter Recommendation: Online Motif Detection in Large Dynamic Graphs | 2014 | VLDB |
| 7 | 5,598 | Hypergraph Motifs: Concepts, Algorithms, and Discoveries | 2020 | VLDB |
| 8 | 8,019 | Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks | 2024 | VLDB |
| 9 | 3,028 | Motivo: fast motif counting via succinct color coding and adaptive sampling | 2019 | VLDB |
| 10 | 11,690 | On Analyzing Graphs with Motif-Paths | 2021 | VLDB |