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
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Authors
- 1. Mohammad Matin Najafi
- 2. Chenhao Ma
- 3. Xiaodong Li
- 4. Reynold Cheng
- 5. Laks V.S. Lakshmanan
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,015 | ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model | 2024 | VLDB | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 283 | Querying K-Truss Community in Large and Dynamic Graphs | 2014 | SIGMOD | 0.00029041257 |
| 1,844 | Effective Community Search over Large Spatial Graphs | 2017 | VLDB | 0.00010341077 |
| 11,492 | On Analyzing Graphs with Motif-Paths | 2021 | VLDB | 4.1945683e-05 |
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